A Semantic Parser generates a logical form graph from an utterance where the edges are semantic roles and nodes are word senses in an ontology that supports reasoning. The generated representation attempts to capture the full meaning of the utterance. While the process of parsing works to resolve lexical ambiguity, a number of errors in the logical forms arise from incorrectly assigned word sense determinations. This is especially true in logical and rule-based semantic parsers. Although the performance of statistical word sense disambiguation methods is superior to the word sense output of semantic parser, these systems do not produce the rich role structure or a detailed semantic representation of the sentence content. In this work, we use decisions from a statistical WSD system to inform a logical semantic parser and greatly improve semantic type assignments in the resulting logical forms.

Progress on deep language understanding is inhibited by the lack of a broad coverage lexicon that connects linguistic behavior to ontological concepts and axioms. We have developed COLLIE-V, a deep lexical resource for verbs, with the coverage of WordNet and syntactic and semantic details that meet or exceed existing resources. Bootstrapping from a hand-built lexicon and ontology, new ontological concepts and lexical entries, together with semantic role preferences and entailment axioms, are automatically derived by combining multiple constraints from parsing dictionary definitions and examples. We evaluated the accuracy of the technique along a number of different dimensions and were able to obtain high accuracy in deriving new concepts and lexical entries. COLLIE-V is publicly available.

Current semantic parsers either compute shallow representations over a wide range of input, or deeper representations in very limited domains. We describe a system that provides broad-coverage, deep semantic parsing designed to work in any domain using a core domain-general lexicon, ontology and grammar. This paper discusses how this core system can be customized for a particularly challenging domain, namely reading research papers in biology. We evaluate these customizations with some ablation experiments

The bulk of current research in dialogue systems is focused on fairly simple task models, primarily state-based.Progress on developing dialogue systems for more complex tasks has been limited by the lack generic toolkits to build from.In this paper we report on our development from the ground up of a new dialogue model based on collaborative problem solving.We implemented the model in a dialogue system shell (Cogent) that allows developers to plug in problem-solving agents to create dialogue systems in new domains.The Cogent shell has now been used by several independent teams of researchers to develop dialogue systems in different domains, with varied lexicons and interaction style, each with their own problem-solving backend.We believe this to be the first practical demonstration of the feasibility of a CPSbased dialogue system shell.

We present a modular, end-to-end dialogue system for a situated agent to address a multimodal, natural language dialogue task in which the agent learns complex representations of block structure classes through assertions, demonstrations, and questioning.The concept to learn is provided to the user through a set of positive and negative visual examples, from which the user determines the underlying constraints to be provided to the system in natural language.The system in turn asks questions about demonstrated examples and simulates new examples to check its knowledge and verify the user's description is complete.We find that this task is non-trivial for users and generates natural language that is varied yet understandable by our deep language understanding architecture.

The general problem of finding satisfying solutions to constraint-based underspecified representations of quantifier scope is NP-complete. Existing frameworks, including Dominance Graphs, Minimal Recursion Semantics, and Hole Semantics, have struggled to balance expressivity and tractability in order to cover real natural language sentences with efficient algorithms. We address this trade-off with a general principle of coherence, which requires that every variable introduced in the domain of discourse must contribute to the overall semantics of the sentence. We show that every underspecified representation meeting this criterion can be efficiently processed, and that our set of representations subsumes all previously identified tractable sets.

We demonstrate an integrated system for building and learning models and structures in both a real and virtual environment. The system combines natural language understanding, planning, and methods for composition of basic concepts into more complicated concepts. The user and the system interact via natural language to jointly plan and execute tasks involving building structures, with clarifications and demonstrations to teach the system along the way. We use the same architecture for building and simulating models of biology, demonstrating the general-purpose nature of the system where domain-specific knowledge is concentrated in sub-modules with the basic interaction remaining domain-independent. These capabilities are supported by our work on semantic parsing, which generates knowledge structures to be grounded in a physical representation, and composed with existing knowledge to create a dynamic plan for completing goals. Prior work on learning from natural language demonstrations enables learning of models from very few demonstrations, and features are extracted from definitions in natural language. We believe this architecture for interaction opens up a wide possibility of human-computer interaction and knowledge transfer through natural language.

Understanding common entities and their attributes is a primary requirement for any system that comprehends natural language.In order to enable learning about common entities, we introduce a novel machine comprehension task, GuessTwo: given a short paragraph comparing different aspects of two realworld semantically-similar entities, a system should guess what those entities are.Accomplishing this task requires deep language understanding which enables inference, connecting each comparison paragraph to different levels of knowledge about world entities and their attributes.So far we have crowdsourced a dataset of more than 14K comparison paragraphs comparing entities from a variety of categories such as fruits and animals.We have designed two schemes for evaluation: open-ended, and binary-choice prediction.For benchmarking further progress in the task, we have collected a set of paragraphs as the test set on which human can accomplish the task with an accuracy of 94.2% on open-ended prediction.We have implemented various models for tackling the task, ranging from semantic-driven to neural models.The semantic-driven approach outperforms the neural models, however, the results indicate that the task is very challenging across the models.

Domain-independent meaning representation of text has received a renewed interest in the NLP community.Comparison plays a crucial role in shaping objective and subjective opinion and measurement in natural language, and is often expressed in complex constructions including ellipsis.In this paper, we introduce a novel framework for jointly capturing the semantic structure of comparison and ellipsis constructions.Our framework models ellipsis and comparison as interconnected predicate-argument structures, which enables automatic ellipsis resolution.We show that a structured prediction model trained on our dataset of 2,800 gold annotated review sentences yields promising results.Together with this paper we release the dataset and an annotation tool which enables two-stage expert annotation on top of tree structures.

Nasrin Mostafazadeh, Nathanael Chambers, Xiaodong He, Devi Parikh, Dhruv Batra, Lucy Vanderwende, Pushmeet Kohli, James Allen. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

Typically, visually-grounded language learning systems only accept feature data about objects in the environment that are explicitly mentioned, whether through annotation labels or direct reference through natural language.We show that when objects are described ambiguously using natural language, a system can use a combination of the pragmatic principles of Contrast and Conventionality, and multiple-instance learning to learn from ambiguous examples in an online fashion.Applying child language learning strategies to visual learning enables more effective learning in real-time environments, which can lead to enhanced teaching interactions with robots or grounded systems in multi-object environments.

Comparison is one of the most important phenomena in language for expressing objective and subjective facts about various entities.Systems that can understand and reason over comparative structure can play a major role in the applications which require deeper understanding of language.In this paper we present a novel semantic framework for representing the meaning of comparative structures in natural language, which models comparisons as predicate-argument pairs interconnected with semantic roles.Our framework supports not only adjectival, but also adverbial, nominal, and verbal comparatives.With this paper, we provide a novel dataset of gold-standard comparison structures annotated according to our semantic framework.

We motivate the integration of programming by example and natural language programming by developing a system for specifying programs for simple text editing operations based on regular expressions. The programs are described with unconstrained natural language instructions, and providing one or more examples of input/output. We show that natural language allows the system to deduce the correct program much more often and much faster than is possible with the input/output example(s) alone, showing that natural language programming and programming by example can be combined in a way that overcomes the ambiguities that both methods suffer from individually, while providing a more natural interface to the user.

We describe ongoing research towards building a cognitively plausible system for near one-shot learning of the meanings of attribute words and object names, by grounding them in a sensory model. The system learns incrementally from human demonstrations recorded with the Microsoft Kinect, in which the demonstrator can use unrestricted natural language descriptions. We achieve near-one shot learning of simple objects and attributes by focusing solely on examples where the learning agent is confident, ignoring the rest of the data. We evaluate the system's learning ability by having it generate descriptions of presented objects, including objects it has never seen before, and comparing the system response against collected human descriptions of the same objects. We propose that our method of retrieving object examples with a k-nearest neighbor classifier using Mahalanobis distance corresponds to a cognitively plausible representation of objects. Our initial results show promise for achieving rapid, near one-shot, incremental learning of word meanings.

We present a general framework for complex event recognition that is well-suited for integrating information that varies widely in detail and granularity. Consider the scenario of an agent in an instrumented space performing a complex task while describing what he is doing in a natural manner. The system takes in a variety of information, including objects and gestures recognized by RGB-D and descriptions of events extracted from recognized and parsed speech. The system outputs a complete reconstruction of the agent's plan, explaining actions in terms of more complex activities and filling in unobserved but necessary events. We show how to use Markov Logic (a probabilistic extension of first-order logic) to create a model in which observations can be partial, noisy, and refer to future or temporally ambiguous events; complex events are composed from simpler events in a manner that exposes their structure for inference and learning; and uncertainty is handled in a sound probabilistic manner. We demonstrate the effectiveness of the approach for tracking kitchen activities in the presence of noisy and incomplete observations.

It is over thirty years since I developed interval temporal logic and the accompanying logic of action and time. Overall, these theories have held up well and, with some extensions over the years, have remained useful in our work on AI planning/reasoning systems and natural language understanding. Recently I have become interested in systems that can learn by reading, and specifically, that can learn necessary conditions for event occurrence from reading dictionary definitions. This task adds new constraints on the form of the temporal logic we need. In this talk, I will review our earlier work on temporal logic and then look at the problems that have forced a recent generalization of the formalism in order to allow compositional construction of event definitions from natural language definitions.

Despite decades of research attempting to establish conversational interaction between humans and computers, the capabilities of automated conversational systems are still limited. In this paper, we introduce Chorus, a crowd-powered conversational assistant. When using Chorus, end users converse continuously with what appears to be a single conversational partner. Behind the scenes, Chorus leverages multiple crowd workers to propose and vote on responses. A shared memory space helps the dynamic crowd workforce maintain consistency, and a game-theoretic incentive mechanism helps to balance their efforts between proposing and voting. Studies with 12 end users and 100 crowd workers demonstrate that Chorus can provide accurate, topical responses, answering nearly 93% of user queries appropriately, and staying on-topic in over 95% of responses. We also observed that Chorus has advantages over pairing an end user with a single crowd worker and end users completing their own tasks in terms of speed, quality, and breadth of assistance. Chorus demonstrates a new future in which conversational assistants are made usable in the real world by combining human and machine intelligence, and may enable a useful new way of interacting with the crowds powering other systems.

Planning is vital to a wide range of domains, including robotics, military strategy, logistics, itinerary generation and more, that both humans and computers find difficult. Collaborative planning holds the promise of greatly improving performance on these tasks by leveraging the strengths of both humans and automated planners. However, this requires formalizing the problem domain and input, which must be done by hand, a priori, restricting its use in general real-world domains. We propose using a real-time crowd of workers to simultaneously solve the planning problem, formalize the domain, and train an automated system. As plans are developed, the system is able to learn the domain, and contribute larger segments of work.

We describe a method for determining the names of RFID-tagged objects in activity videos using descriptions which have been parsed to provide anaphoric reference resolution and ontological categorization.

In corpus linguistics obtaining high-quality semantically-annotated corpora is a fundamental goal. Various annotations of the same text can be obtained from automated systems, human annotators, or a combination of both. Obtaining, by manual means, a merged annotation from these, which improves the correctness of each individual annotation, is costly. We present automatic algorithms specifically for merging temporal annotations. These have been evaluated merging the annotations of three state-of-the-art systems on the gold standard corpora and the correctness of the merged annotation improved over that of individual annotations and baseline merging algorithms.

We describe a novel domain, Fruit Carts, aimed at eliciting human language production for the twin purposes of (a) dialogue system research and development and (b) psycholinguistic research. Fruit Carts contains five tasks: choosing a cart, placing it on a map, painting the cart, rotating the cart, and filling the cart with fruit. Fruit Carts has been used for research in psycholinguistics and in dialogue systems. Based on these experiences, we discuss how well the Fruit Carts domain meets four desired features: unscripted, context-constrained, controllable difficulty, and separability into semi-independent subdialogues. We describe the domain in sufficient detail to allow others to replicate it; researchers interested in using the corpora themselves are encouraged to contact the authors directly.

In this paper, we introduce the idea of automatically illustrating complex sentences as multimodal summaries that combine pictures, structure and simplified compressed text. By including text and structure in addition to pictures, multimodal summaries provide additional clues of what happened, who did it, to whom and how, to people who may have difficulty reading or who are looking to skim quickly. We present ROC-MMS, a system for automatically creating multimodal summaries (MMS) of complex sentences by generating pictures, textual summaries and structure. We show that pictures alone are insufficient to help people understand most sentences, especially for readers who are unfamiliar with the domain. An evaluation of ROC-MMS in the Wikipedia domain illustrates both the promise and challenge of automatically creating multimodal summaries.

Abstract The generation of referring expressions is a central topic in computational linguistics. Natural referring expressions – both definite references like ‘the baseball cap’ and pronouns like ‘it’ – are dependent on discourse context. We examine the practical implications of context-dependent referring expression generation for the design ofspoken systems. Currently, not all spoken systems have the goal of generating natural referring expressions. Many researchers believe that the context-dependency of natural referring expressions actually makes systemslessusable. Using the dual-task paradigm, we demonstrate that generating natural referring expressions that are dependent on discourse context reduces cognitive load. Somewhat surprisingly, we also demonstrate that practice does not improve cognitive load in systems that generate consistent (context-independent) referring expressions. We discuss practical implications for spoken systems as well as other areas of referring expression generation.

We describe a two-layer architecture for supporting semantic interpretation and domain reasoning in dialogue systems. Building system that supports both semantic interpretation and domain reasoning in a transparent and well-integrated manner is an unresolved problem because of the diverging requirements of the semantic representations used in contextual interpretation versus the knowledge representations used in domain reasoning. We propose an architecture that provides both portability and efficiency in natural language interpretation by maintaining separate semantic and domain knowledge representations, and integrating them via an ontology mapping procedure. The ontology mapping is used to obtain representations of utterances in a form most suitable for domain reasoners and to automatically specialize the lexicon. The use of a linguistically motivated parser for producing semantic representations for complex natural language sentences facilitates building portable semantic interpretation components as well as connections with domain reasoners. Two evaluations demonstrate the effectiveness of our approach: we show that a small number of mapping rules are sufficient for customizing the generic semantic representation to a new domain, and that our automatic lexicon specialization technique improves parser speed and accuracy.

Learning tasks from a single demonstration presents a significant challenge because the observed sequence is specific to the current situation and is inherently an incomplete representation of the procedure. Observation-based machine-learning techniques are not effective without multiple examples. However, when a demonstration is accompanied by natural language explanation, the language provides a rich source of information about the relationships between the steps in the procedure and the decision-making processes that led to them. In this article, we present a one-shot task learning system built on TRIPS, a dialogue-based collaborative problem solving system, and show how natural language understanding can be used for effective one-shot task learning.

The prevalent state of the art in spoken language understanding by spoken dialog systems is both modular and whole-utterance. It is modular in that incoming utterances are processed by independent components that handle different aspects, such as acoustics, syntax, semantics, and intention / goal recognition. It is whole-utterance in that each component completes its work for an entire utterance prior to handing off the utterance to the next component. However, a growing body of evidence suggests that humans do not process language that way. Rather, people process speech by rapidly integrating constraints from multiple sources of knowledge and multiple linguistic levels incrementally, as the utterance unfolds. In this paper we describe ongoing work aimed at developing an architecture that will allow machines to understand spoken language in a similar way. This revolutionary approach is promising for two reasons: 1) it more accurately reflects contemporary models of human language understanding, and 2) it results in empirical improvements including increased parsing performance.

As speech interfaces become more prevalent, it i s becoming more crucial that they be developed in a way that minimizes cognitive load for users. One major barrier to creating systems that are more human-centered has been the lack of an accepted online methodology for directly evaluating the cognitive resource demands of different systems. The present study extends a classic tool from cognitive psychology, the dual-task paradigm, to speech interface evaluation. Participants follow simple instructions generated by a system, while simultaneously monitoring for a simple visual probe. Performance on the monitoring task is used as a measure of cognitive resource demands; whenever language understanding is more demanding, performance on the monitoring task suffers. In the present study we used this methodology to investigate patterns of reference generation and how they impact human understanding.

We present experimental evidence from a study in which we monitor eye movements as people respond to pre-recorded instructions generated by a human speaker and by two text-tospeech synthesizers. We replicate findings demonstrating that people process human speech incrementally, making partial commitments as a word unfolds. Specifically, they entertain multiple lexical candidates on the fly depending on segmental overlap in the candidate set. Importantly, incremental understanding is also observed for synthesized text-to-speech instructions. These results, including some suggestive differences in responses with the two text-to-speech systems, establish the potential for using eye-tracking methodology together with synthesized speech stimuli as a powerful theoretical and experimental tool for spoken language processing research. 1. BACKGROUND Spoken utterances unfold over time, and psycholinguistic studies of natural speech have shown that the time course of spoken language results in a stream of temporary ambiguities at

In this paper, we describe an architecture for conversational systems that enables human-like performance along several important dimensions. First, interpretation is incremental, multi-level, and involves both general and task- and domain-specific knowledge. Second, generation is also incremental, proceeds in parallel with interpretation, and accounts for phenomena such as turn-taking, grounding and interruptions. Finally, the overall behavior of the system in the task at hand is determined by the (incremental) results of interpretation, the persistent goals and obligations of the system, and exogenous events of which it becomes aware. As a practical matter, the architecture supports a separation of responsibilities that enhances portability to new tasks and domains.

We introduce a hierarchical statistical language model, represented as a collection of local models plus a general sentence model. We provide an example that mixes a trigram general model and a PFSA local model for the class of decimal numbers, described in terms of sub-word units (graphemes). This model practically extends the vocabulary of the overall model to an infinite size, but still has better performance compared to a word-based model. Using in-domain language model adaptation experiments, we show that local models can encode enough linguistic information, if well trained, that they may be ported to new language models without re-estimation.

We introduce in this paper a hierarchical hybrid statistical language model, represented as a collection of local models plus a general model that binds together the local ones. The model provides a unified framework for modelling language both above and below the word level, and we exemplify with models of both kinds for a large vocabulary task domain. To our knowledge this is the first paper to report an extensive evaluation of the improvements achieved from the use of local models within a hierarchical framework in comparison with a conventional word-based trigram model.

This paper describes our work on dialogue systems that can mimic human conversation, with the goal of providing intuitive access to a wide range of applications by expanding the user's options in the interaction. We concentrate on practical dialogue: dialogues in which the participants need to accomplish some objective or perform some task. Two hypotheses regarding practical dialogue motivate our research. First, that the conversational competence required for practical dialogues, while still complex, is significantly simpler to achieve than general human conversational competence. And second, that within the genre of practical dialogue, the bulk of the complexity in the language interpretation and dialogue management is independent of the task being performed. If these hypotheses are true, then it should be possible to build a generic dialogue shell for practical dialogue, by which we mean the full range of components required in a dialogue system, including speech recognition, language processing, dialogue management and response planning, built in such a way as to be readily adapted to new applications by specifying the domain and task models. This paper documents our progress and what we have learned so far based on building and adapting systems in a series of different problem solving domains.

To understand a speaker's turn of a conversation, one needs to segment it into intonational phrases, clean up any speech repairs that might have occurred, and identify discourse markers. In this paper, we argue that these problems must be resolved together, and that they must be resolved early in the processing stream. We put forward a statistical language model that resolves these problem, does POS tagging, and can be used as the language model of a speech recognizer. We find that by accounting for the interactions between these tasks that the performance on each task improves, as does POS tagging and perplexity.

Language models for speech recognition tend to concentrate solely on recognizing the words that were spoken.In this paper, we redefine the speech recognition problem so that its goal is to find both the best sequence of words and their syntactic role (part-of-speech) in the utterance.This is a necessary first step towards tightening the interaction between speech recognition and natural language understanding.

This paper describes a system that leads us to believe in the feasibility of constructing natural spoken dialogue systems in task-oriented domains. It specifically addresses the issue of robust interpretation of speech in the presence of recognition errors. Robustness is achieved by a combination of statistical error post-correction, syntactically- and semantically-driven robust parsing, and extensive use of the dialogue context. We present an evaluation of the system using time-to-completion and the quality of the final solution that suggests that most native speakers of English can use the system successfully with virtually no training.

This paper presents a new technique for overcoming several types of speech recognition errors by post-processing the output of a continuous speech recognizer. The post-processor output contains fewer errors, thereby making interpretation by higher-level modules, such as a parser, in a speech understanding system more reliable. The primary advantage to the post-processing approach over existing approaches for overcoming SR errors lies in its ability to introduce options that are not available in the SR module's output. This work provides evidence for the claim that a modern continuous speech recognizer can be used successfully in "black-box" fashion for robustly interpreting spontaneous utterances in a dialogue with a human.

We have implemented a post-processor called SPEECHPP to correct word-level error^ committed by an arbitrary speech recognizer.A p plying a noisychannelmodel, SPEECHPPuses a Viterbi beam-search that employs language and channel models.Previous work demonstrated that a simple word-for-word channel model was sufficient to yield substantial incieases in word accuracy.This paper demonstrates that some improvements in word accuracy result from augmenting the channel model with an account of word fertility in the channel.This work further demonstrates that a modern continuous speech recognizer can be used in "black-box" fashion for robustly recognizing speech for which the recognizer was not originally trained.This work also demonstrates that in the case when the recognizercan be tuned to the new task, environment, or spealcer, the post-processor can also contribute to performance improvements.

The TRAINS project is an effort to build a conversationally proficient planning assistant. A key part of the project is the construction of the TRAINS system, which provides the research platform for a wide range of issues in natural language understanding, mixed-initiative planning systems, and representing and reasoning about time, actions and events. Four years have now passed since the beginning of the project. Each year a demonstration system has been produced that focused on a dialogue that illustrates particular aspects of the research. The commitment to building complete integrated systems is a significant overhead on the research, but it is considered essential to guarantee that the results constitute real progress in the field. This paper describes the goals of the project, and the experience with the effort so far.

Interactive spoken dialog provides many new challenges for spoken language systems.One of the most critical is the prevalence of speech repairs.This paper presents an algorithm that detects and corrects speech repairs based on finding the repair pattern.The repair pattern is built by finding word matches and word replacements, and identifying fragments and editing terms.Rather than using a set of prebuilt templates, we build the pattern on the fly.In a fair test, our method, when combined with a statistical model to filter possible repairs, was successful at detecting and correcting 80% of the repairs, without using prosodic information or a parser.

We show that in modeling social interaction, particularly dialogue, the attitude of obligation can be a useful adjunct to the popularly considered attitudes of belief, goal, and intention and their mutual and shared counterparts. In particular, we show how discourse obligations can be used to account in a natural manner for the connection between a question and its answer in dialogue and how obligations can be used along with other parts of the discourse context to extend the coverage of a dialogue system.

We present a representation of events and action based on interval temporal logic that is significantly more expressive and more natural than most previous AI approaches. The representation is motivated by work in natural language semantics and discourse, temporal logic, and AI planning and plan recognition. The formal basis of the representation is presented in detail, from the axiomatization of time periods to the relationship between actions and events and their effects. The power of the representation is illustrated by applying it to the axiomatization and solution of several standard problems from the AI literature on action and change. An approach to the frame problem based on explanation closure is shown to be both powerful and natural when combined with our representational framework. We also discuss features of the logic that are beyond the scope of many traditional representations, and describe our approach to difficult problems such as external event! and simultaneous actions.

One of the most crucial problems in any computer system that involves representing the world is the representation of time. This includes applications such as databases, simulation, expert systems, and applications of Artificial Intelligence in general. In this brief article, I will give a survey of the basic techniques available for representing time, and then talk about temporal reasoning in a general setting as needed in AI applications. Quite different representations of time are usable depending on the assumptions that can be made about the temporal information to be represented. the most crucial issue is the degree of certainty one can assume. Can one assume that a timestamp can be assigned to each event, or barring that, that the events are fully ordered? Or can we only assume that a partial ordering of events is known? Can events be simultaneous? Can they overlap in time and yet not be simultaneous? If they are not instaneous, do we know the durations of events? Different answers to each of these questions allow very different representations of time.

Existing plan-based theories of speech act interpretation do not account for the conventional aspect of speech acts. We use patterns of linguistic features (e.g. mood, verb form, sentence adverbials, thematic roles) to suggest a range of speech act interpretations for the utterance. These are filtered using plan-based conversational implicatures to eliminate inappropriate ones. Extended plan reasoning is available but not necessary for familiar forms. Taking speech act ambiguity seriously, with these two constraints, explains how "Can you pass the salt?" is a typical indirect request while "Are you able to pass the salt?" is not.

Abstract This paper develops and explores a first‐order theory of time that appears useful as an underlying framework for a wide range of practical applications in artificial intelligence (AI). In particular, it presents a concise, formal axiomatization of “interval‐based” time as described by Allen and then explores the relationship between interval‐based and point‐based temporal theories in detail. This analysis should be useful to both theoretical and practical work in AI that involves the representation of time, since it shows what distinctions are actually substantive and what arise merely from formalisms being notational variants of one another.

One of the most promising computational approaches to representing context in natural language systems has been based on work in general problem solving. In this approach, plans are used both to represent the domain of discourse as well as the communication process itself. Using a simplified framework for planning and action reasoning, we describe techniques that allow systems to handle many dialogues that are problematic for other systems, including the use of sentence fragments, indirect speech, helpful responses, the tracking of the topic of conversations both with and without interrupting subdialogues, and topic change.

This paper outlines a temporal logic extended with two modalities that can be used to support planning in temporally rich domains. In particular, the logic can represent planning environments that have assertions about future possibilities in addition to the present state, and plans that contain concurrent actions. The logic is particularly expressive in the ways that concurrent actions can interact with each other and allows situations where either one of the actions can be executed, but both cannot, as well as situations where neither action can be executed alone, but they can be done together. Two modalities are introduced and given a formal semantics: INEV expresses simple temporal possibility, and IFTRIED expresses counteffactual-like statements about actions.

One of the promising approaches to analyzing task-oriented dialogues has involved modeling the plans of the speakers in the task domain. In general, these models work well as long as the topic follows the task structure closely, but they have difficulty in accounting for clarification subdialogues and topic change. We have developed a model based on a hierarchy of plans and metaplans that accounts for the clarification subdialogues while maintaining the advantages of the plan-based approach.

Ever since Wood's "What's in a Link" paper, there has been a growing concern for formalization in the study of knowledge representation. Several arguments have been made that frame representation languages and semantic-network languages are syntactic variants of the first-order predicate calculus (FOPC). The typical argument proceeds by showing how any given frame or network representation can be mapped to a logically isomorphic FOPC representation. For the past two years we have been studying the formalization of knowledge retrievers as well as the representation languages that they operate on. This paper presents a representation language in the notation of FOPC whose form facilitates the design of a semantic-network-like retriever.

This paper considers what types of knowledge one must possess in order to reason about actions. Rather than concentrating on how actions are performed, as is done in the problem-solving literature, it examines the set of conditions under which an action can be said to have occurred. In other words, if one is told that action A occurred, what can be inferred about the state of the world? In particular, if the representation can define such conditions, it must have good models of time, belief, and intention. This paper discusses these issues and suggests a formalism in which general actions and events can be defined. Throughout, the action of hiding a book from someone is used as a motivating example.

Americanae nace como un proyecto conjunto que surge dentro de la Red Europea de Información y Documentación sobre América Latina (REDIAL), y que ha afrontado la Biblioteca de la Agencia Española de Cooperación Internacional para el Desarrollo (AECID). Esta nueva biblioteca virtual hace más accesibles los libros digitales de tema americanista a los investigadores y usuarios interesados de cualquier parte del mundo.