We extend goal recognition design to account for partially informed agents. In particular, we consider a two-agent setting in which one agent, the actor, seeks to achieve a goal but has only incomplete information about the environment. The second agent, the recognizer, has perfect information and aims to recognize the actor's goal from its behavior as quickly as possible. As a one-time offline intervention and with the objective of facilitating the recognition task, the recognizer can selectively reveal information to the actor. The problem of selecting which information to reveal, which we call information shaping, is challenging not only because the space of information shaping options may be large, but also because more information revelation need not make it easier to recognize an agent's goal. We formally define this problem, and suggest a pruning approach for efficiently searching the search space. We demonstrate the effectiveness and efficiency of the suggested method on standard benchmarks.

This paper describes methods for comparative evaluation of the interpretability of models of high dimensional time series data inferred by unsupervised machine learning algorithms. The time series data used in this investigation were logs from an immersive simulation like those commonly used in education and healthcare training. The structures learnt by the models provide representations of participants' activities in the simulation which are intended to be meaningful to people's interpretation. To choose the model that induces the best representation, we designed two interpretability tests, each of which evaluates the extent to which a model’s output aligns with people’s expectations or intuitions of what has occurred in the simulation. We compared the performance of the models on these interpretability tests to their performance on statistical information criteria. We show that the models that optimize interpretability quality differ from those that optimize (statistical) information theoretic criteria. Furthermore, we found that a model using a fully Bayesian approach performed well on both the statistical and human-interpretability measures. The Bayesian approach is a good candidate for fully automated model selection, i.e., when direct empirical investigations of interpretability are costly or infeasible.

To become proficient at landmine detection, novice deminers need to master several kinds of skills: the proper physical operation of the metal detector, the interpretation of the metal detector auditory feedback, and the abstract skill of constructing and interpreting mental representations of the "metallic signatures" produced by the buried objects. This last skill is particularly useful for safely dealing with mines laid out in cluster configurations, where their metallic signatures overlap and thus a danger exists that a deminer might either miss some of the mines or incorrectly assess their exact positions. However, some novice deminers find it challenging to learn how to properly reason about metallic signatures. We have developed Petals, a system that explicitly visualizes a trainee's metal detector operation history on a training task as well as the edge points of the metallic signatures that the trainee collected. Petals enables instructors to supervise multiple trainees at a time, to assess their performance at a glance, and to provide immediate and specific feedback both on the correctness of their final judgements about the number and positions of landmines, and on the process through which they arrived at their conclusions. The results of our field evaluations at the Humanitarian Demining Training Center showed that both the instructors and the trainees found the system a valuable addition to the training course. The results of a controlled study demonstrated that trainees who had access to Petals during training made significantly fewer errors (6% error rate) on relevant tasks during the final exam (which was conducted without Petals) than trainees who did not have access to Petals during training (those participants had a 21% error rate).

I am deeply grateful for the honor of this award, all the more so for its being completely unexpected. I am especially pleased by the recognition this award gives to our early attempts to build computational models of dialogue and to develop algorithms that would enable computer systems to converse sensibly with people. ACL was my first academic home. I presented my first paper, the paper that laid out the basics of a computational model of discourse structure, at the 1975 ACL meeting. Then, after several decades of research centered on dialogue systems, my research focus shifted to modeling collaboration. This shift was driven in part by the need for computational models of collaborative activities to support dialogue processing, a topic which I explore briefly below, and in part by limitations in speech processing and semantics capabilities. Research in these areas has advanced significantly in the last decade, enabling advances in dialogue as well, and I have recently returned to investigating computer system dialogue capabilities and challenges. I am glad to be back in my intellectual home.The use of language has been considered an essential aspect of human intelligence for centuries, and the ability for a computer system to carry on a dialogue with a person has been a compelling goal of artificial intelligence (AI) research from its inception. Turing set conversational abilities as the hallmark of a thinking machine in defining his “imitation game,” more commonly referred to as “The Turing Test.” In the 1950 Mind paper in which he defines this game, Turing conjectures thus, “I believe that at the end of the century the use of words and general educated opinion will have altered so much that one will be able to speak of machines thinking without expecting to be contradicted.” (Turing 1950, p. 442). Though the Turing Test remains an elusive (and now debatable) goal, this conjecture has proved true. Natural language processing research has made great progress in the last decades, with personal assistant systems and chatbots becoming pervasive, joining search and machine translation systems in common use by people around the world. Although it is commonplace to hear people talk about these systems in anthropomorphic terms — using “she” more frequently than “it” in referring to them — the limitations of these systems’ conversational abilities frequently leads people to wonder what they were thinking or if they even were thinking. As the dialogue in Figure 1 illustrates, at root these systems lack fundamental dialogue capabilities. The first assignment in a course I have been teaching, “Intelligent Systems: Design and Ethical Challenges,” is to test phone-based personal assistants. Although some systems might handle this particular example, they all fail similarly, and the range of dialogue incapabilities students have uncovered is stunning. As I have argued elsewhere, the errors that systems make reveal how far we still have to go to clear Turing’s hurdle or to have systems smart enough to (really) talk with us (Grosz, 2012).The first generation of speech systems, which were developed in the 1970s, divided system components by linguistic category (e.g., acoustic-phonetics, syntax, lexical and compositional semantics), and dialogue challenges of that era included identifying ways to handle prosody and referring expressions, along with nascent efforts to treat computationally language as action. In contrast, current spoken language research takes a more functional perspective, considering such issues as sentiment analysis, entrainment, and deception detection. Typical current dialogue challenges include such functions as turn-taking, clarification questions, and negotiation. The database query applications of earlier decades, which lacked grounding in dialogue purpose, have been replaced by template filling and chat-oriented applications that are designed for relatively narrowly defined tasks.Various findings and lessons learned in early attempts to build dialogue systems have potential to inform and significantly improve the capabilities of systems that currently aim to converse with people. Although the computational linguistic methods that have recently enabled great progress in many areas of speech and natural-language processing differ significantly from those used even a short while ago, the dialogue principles uncovered in earlier research remain relevant. The next two sections of this paper give brief summaries of the principal findings and foundations in models of dialogue and collaboration established in the 1970s through the 1990s. The following section examines principles for dialogue systems derived from these models that could be used with current speech and language processing methods to improve the conversational abilities of personal assistants, customer service chatbots, and other dialogue systems. The paper concludes with scientific and ethical challenges raised by the development and deployment of dialogue-capable computer systems.My first papers on dialogue structure (Grosz [Deutsch], 1974, 1975; Grosz, 1977) were based on studies of task-oriented dialogues I collected to inform the design of the discourse component of a speech understanding system. In these dialogues, an expert (who was eventually to be replaced by the system) instructed an apprentice in equipment assembly. The expert and apprentice were in separate rooms, they communicated through a teletype interface (with a camera available for still shots on demand), and the apprentice was led to believe the expert was a computer system. These dialogues were collected as part of what were, to my knowledge, the first “Wizard of Oz” experiments.1Figure 2 is one example from this collection. It illustrates the influence of dialogue structure on definite noun phrase interpretation. In Utterance (3), the expert (E) directs the apprentice (A) to loosen two setscrews. In Utterance (8), the expert instructs the apprentice to loosen “the screw in the center”. The only intermediate explicit mention of screw-like objects are subsequent references to the setscrews in Utterance (5) and the elided subdialogue about them between Utterances (3) and (5). With only two objects, one cannot be in the center. Indeed, the screw to which the expert refers in Utterance (8) is in the center of the wheelpuller. This dialogue fragment has a structure that parallels the task of removing the flywheel. When the first step of that task (loosening the setscrews) is completed, as indicated by the apprentice in Utterance (5), the focus of attention of expert and apprentice move to the step of pulling off the wheel with the wheelpuller. At the point of mention of “the screw in the center”, the setscrews are no longer relevant or in context; the wheelpuller is.A more striking example is provided by the dialogue sample in Figure 3. Each of the successful assemblies of the air compressor ended in one of the ways shown in this figure. Despite admonitions of grammar teachers and editors that pronouns be used to refer to the last object mentioned that matches in number and gender, actual use of pronouns can vary markedly. In this case, the pronoun “it” is used to refer to the air compressor after half an hour of dialogue in which there was no explicit mention of it.Again, context and the focus of attention has shifted over the course of the assembly and the expert-apprentice dialogue related to it. At the point at which the last utterance occurs, the air compressor is assembled and is the focus of attention. Not only did the participants in these dialogues have no trouble understanding what was to be plugged in or turned on (even though they were in a room full of equipment that might have provided other options), but also readers of the transcripts of these dialogues are not confused.The narrative fragment in Figure 4, which is from the work of Polanyi and Scha (1983), shows that this kind of discourse structure influence on referring expressions is not restricted to task dialogues. The linearly nearest referent for the pronoun “them” in the last utterance is the children mentioned in the previous utterance. It is the groceries that were put away though, not the children. This example also illustrates the role of prosody in signaling discourse structure. Intonation is crucial to getting the interpretation of this story right. The second utterance is said as an aside, with strong intonational markers of this separation. Prosody, then, is integral to dialogue processing, and spoken dialogue cannot be handled as a pipeline from speech recognition to pragmatic plan recognition.These fragments illustrate a primary finding of my early dialogue research: dialogues are not linear sequences of utterances nor simply question–answer pairs. This work established that task-oriented dialogues are structured, with multiple utterances grouping into a dialogue segment, and their structure mirrors the structure of the task. Subsequently, Candy Sidner and I generalized from task dialogues to discourse more generally, defining a computational model of discourse structure (Grosz and Sidner, 1986). This model defines discourse structure in terms of three components as shown in the schematic in Figure 5. The linguistic structure comprises the utterances themselves, including their various linguistic features, which cluster into dialogue segments. Just as written language is broken into paragraphs, the utterances of spoken language naturally form groups. The attentional state tracks the changes in focus of attention of the dialogue participants as their conversation evolves. The intentional structure comprises the purposes underlying each dialogue segment and their relationships to one another. Relationships between dialogue segment purposes (DSPs) determine embedding relationships between the corresponding discourse segments (DS), and thus they are foundational to determining changes in attentional state. These three components of discourse structure are interdependent. The form of an utterance and its constituent phrases are affected by attentional state and intentional structure, and they in turn may engender changes in these components of discourse structure.Sidner and I examined two levels of attentional state. The global level, which is portrayed in Figure 5, is modeled by a focus space stack. Focus spaces contain representations of the objects, properties, and relations salient within a discourse segment as well as the discourse segment purpose. When a new dialogue segment is started, a space is pushed onto the stack, and when the segment completes, the corresponding focus space is popped from the stack. This level of attentional state influences definite noun phrase interpretation and generation and intention recognition processes. Access to objects, properties, and relations at lower levels of the stack may not be available at higher levels. The local level of attentional state, which Sidner and I referred to as “immediate focus” (“local focus” being a tongue twister), models smaller shifts of attention within a discourse segment. Sidner (1979, 1981, 1983a) deployed immediate focusing as a means of controlling the inferences required for interpreting anaphoric expressions like pronouns and demonstratives by limiting the number of entities considered as possible referents. Webber’s contemporaneous work on the range of anaphors a single seemingly simple definite noun phrase could yield made clear the importance of being able to limit candidates (Webber, 1979, 1986). 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Crowdsourcing platforms were initially designed to recruit people to perform tasks that were simple cognitively but difficult for computers. One challenge in these settings is to identify an incentive mechanism for motivating workers to complete tasks and do high-quality work. Previous research has studied the use of financial incentive mechanisms and social comparison as motivators. These mechanisms can only be applied to ground truth tasks, tasks for which there is an objective performance scale. In this paper, we define and compare three innovative methods for improving worker engagement on non-ground truth tasks drawing on a psychological theory of commitment. The three methods are similar in asking participants to promise they will complete a task, but they differ in terms of how the commitment is made. In the first method, participants commit by signing a contract; in the second, by listening to a recording; in the third, by recording a personal commitment. The last two methods significantly improved the task completion rate when compared to two baseline conditions. The methods we propose can be implemented simply, can be used for any task, and do not affect participants' behavior other than by improving their engagement.

People collaborate in carrying out such complex activities as treating patients, co-authoring documents and developing software. While technologies such as Dropbox and Github enable groups to work in a distributed manner, coordinating team members' individual activities poses significant challenges. In this paper, we formalize the problem of "information sharing in loosely-coupled extended-duration teamwork." We develop a new representation, Mutual Influence Potential Networks (MIP-Nets), to model collaboration patterns and dependencies among activities, and an algorithm, MIP-DOI, that uses this representation to reason about information sharing.

Mobile technologies offer the potential for enhanced healthcare, especially by supporting self-management of chronic care. For these technologies to impact chronic care, they need to work for older adults, because the majority of people with chronic conditions are older. A major challenge remains: integrating the appropriate use of such technologies into the lives of older adults. We investigated how older adults would accept mobile technologies by interviewing two groups of older adults (technology adopters and non-adopters who aged 60+) about their experiences and perspectives to mobile technologies. Our preliminary results indicate that there is an additional phase, the intention to learn, and three relating factors, self-efficacy, conversion readiness, and peer support, that significantly influence the acceptance of mobile technologies among the participants, but are not represented in the existing models. With these findings, we propose a tentative theoretical model that extends the existing theories to explain the ways in which our participants came to accept mobile technologies. Future work should investigate the validity of the proposed model by testing our findings against younger people.

Children with complex health conditions require care from a large, diverse team of caregivers that includes multiple types of medical professionals, parents and community support organizations. Coordination of their outpatient care, essential for good outcomes, presents major challenges. Extensive healthcare research has shown that the use of integrated, team-based care plans improves care coordination, but such plans are rarely deployed in practice. This paper reports on a study of care teams treating children with complex conditions at a major university tertiary care center. This study investigated barriers to plan implementation and resultant care coordination problems. It revealed the complex nature of teamwork in complex care, which poses challenges to team coordination that extend beyond those identified in prior work and handled by existing coordination systems. The paper builds on a computational teamwork theory to identify opportunities for technology to support increased plan-based complex-care coordination and to propose design approaches for systems that enable and enhance such coordination.

Jane Robinson, a pioneering computational linguist, made major contributions to machine translation, natural language, and speech systems research programs at the RAND Corporation, IBM, and in the AI Center at SRI International. She served as ACL president in 1982.Jane became a computational linguist accidentally. She had a Ph.D. in history from UCLA, but could not obtain a faculty position in that field because those were reserved for men. Instead, she took positions teaching English, first at UCLA and then at California State College, Los Angeles. While at LA State, where she was tasked with teaching engineers how to write, Jane noticed an announcement for a talk on Chomsky's transformational grammar. She went to the talk thinking this work on grammar might help her teach better. Although its subject matter did not match her expectations, the talk marked a turning point in her career.In the late 1950s, Jane became a consultant to the RAND Corporation group working on machine translation under Dave Hays (ACL president, 1964). From the beginning, Jane was concerned with identifying connections between different traditions in formal grammars and their corresponding detailed linguistic realizations. Her 1965 International Conference on Computational Linguistics (COLING) paper, “Endocentric constructions and the Cocke parsing logic” (Robinson 1965), is a beautiful example of connecting specific linguistic phenomena to parsing strategies in a way that preserves the nature of the linguistic phenomena, endocentric constructions. While at RAND, Jane became colleague and friend to many in the machine translation and emerging computational linguistics world, including Susumo Kuno (ACL president 1967), Martin Kay (ACL president, 1969), Joyce Friedman (ACL president, 1971), and Karen Sparck Jones (ACL president, 1994).In the late 1960s, Jane moved to the Automata Theory and Computability Group at the IBM Thomas J. Watson Research Center in Yorktown Heights, NY. She used her knowledge of formal work on grammars and parsing to draw correspondences between Dependency Grammars and Phrase Structure Grammars. Although Jane came from the Dependency Grammar tradition, her balanced, careful analysis of tradeoffs enabled others to bridge the approaches. Her 1967 COLING paper, “Methods for obtaining corresponding phrase structure and dependency grammars” (Robinson 1967), is a wonderful example of her understanding of the seminal issues underlying these different systems. She subsequently published the classic paper connecting dependency structure and transformational rules, “Dependency structures and transformational rules” (Robinson 1970b). This paper exemplifies Jane's scholarship and her deftness in dealing with the formal and computational issues of language processing in a very fair and informative manner. Her 1970 paper, “Case, category and configuration” (Robinson 1970a), demonstrated in a very convincing way the possibility of formally interpreting Fillmore's case grammar in terms of dependencies, in a more economic fashion and without any loss of information.In 1973, Don Walker (ACL secretary-treasurer, 1976–1993) recruited Jane to the speech group in the AI Center (AIC) at SRI International. Jane remained a key member of the AIC's natural language group until she retired in the mid 1980s. She made major contributions to a wide range of research, ranging from grammars for speech understanding systems and dialogues to such discourse issues as codes and clues in contexts. For several of the NLP systems SRI developed in the 1970s and 1980s, the grammar was the coordinating point for all knowledge about the language, so Jane interacted with everyone developing any component of the system, from the architecture through semantics and discourse. Speech processing was a bit “deaf” in those days, and Jane frequently remarked that she had to write not only a grammar for English, but also its dual (one for non-English to rule out bad parsings). During her time at SRI, Jane wrote some of the most comprehensive grammars for NLP systems.One of us (Barbara Grosz) notes that Jane's contributions to the AIC's natural language group went far beyond her official grammar writing responsibilities. She served as mentor (before that word was widely used in academia) for a large group of “young Turks,” as she referred to those of us in the younger cohort involved in building NLP systems; she was our in-house expert in linguistics; provided critiques of drafts of papers, making them shorter, clearer, and more scholarly; debugged our ideas across the full spectrum of system components; and introduced us to the most senior people in linguistics and computational linguistics.Another of us (Aravind Joshi, ACL president, 1975) recalls meeting Jane in September 1975 at an NLP workshop at the University of Michigan. At that time, he and his students were working on two separate areas of computational linguistics. One of them concerned the minimal formal machinery needed for representing structural aspects of language and the other one dealt with some aspects of cooperation in natural language interfaces to databases. After just a brief discussion with Jane, when she asked what he was doing, it became clear to him, “that I was in the presence of someone who had already worked on such diverse areas. From thereon, whenever I had an opportunity to meet with Jane, I took advantage of her deep understanding.”And the third of us (Eva Hajicova, ACL president, 1998) recalls the important ties Jane formed with Praguian linguists interested in formal grammar starting in 1965, when the founder of the Prague group, Petr Sgall, first visited the United States and met Jane at RAND. Their common research interests in computational linguistics, particularly Dependency Grammar, forged deep personal relationships between Jane and Sgall's linguistics group in Prague. Jane visited Prague twice, once before and once after the change of the communist regime. During difficult political times, Jane provided the Prague group with linguistics literature published in the West, and she introduced them to her colleagues and students, yielding additional important connections, which have continued to this day. Eva notes that, “only those who have the same historical experience as we in Prague have had can appreciate fully how important such activities were for our research and for our students.”Jane read broadly and her training as a historian made her a careful and deep scholar. Throughout her life she would go to talks that seemed a bit far afield and come back with new ideas. For those who worked with her she was an invaluable source of out-of-the-box thinking as well as the go-to person for what to read in linguistics. Jane often said that had she been born in a later generation, she would have become an astronomer. Given her love of exploration, she might have been an astronaut. (You can see this bent in the poem she wrote for her poetry class friend's funeral, “Time To Go” [Robinson 2008]). Lucky for all of us, she wound up in computational linguistics.Jane was the mother of four children and the proud grandmother of two grandsons, one now a lawyer, the other an actor. She extended her family to encompass her colleagues, building camaraderie through dinners at her home—lamb stew, mostly— and picnics at Foothills Park in Palo Alto, activities which drew the families of AIC researchers together and yielded many lifelong friendships. Jane built such friendships throughout her career. In responding to our questions about Jane's time at RAND, Dave Hays's wife Rita noted that Jane remained close friends with her and Dave even after Dave went to SUNY Buffalo and Jane to IBM Yorktown Heights and then SRI. Rita and Jane were traveling and hiking companions into Jane's nineties.To celebrate her sixtieth birthday, Jane “got in shape” to hike in the Himalayas around Annapurna. She came back with beautiful photos and the desire to join the young Turks who went backpacking in Yosemite. (Although she had been a regular visitor to Yosemite since her forties, she had not backpacked before.) She offered to drive everyone in the huge Chrysler Imperial she had gotten to feel safe driving in New York. Jane backpacked into her late seventies, then switched to the luxury of the High Sierra Camp tent cabins and later to a small cabin with an inside shower. For those lucky enough to visit Yosemite with her, she was as much a guide to the mountains as she had been a guide to linguistics.For people who worked with Jane at SRI, she was a towering figure in the field, a wonderful colleague who imparted deep wisdom as well as linguistic facts, and a dear friend. She was senior to most of the members of the AIC. Looking back on her arrival, Peter Hart (a director of the AIC) noted that Jane's presence changed the tone of the early SRI AI Center. She brought not only a keen intellect and depth of knowledge, but “also a gentleness, openness, and generosity of spirit.” Gary Hendrix (who led the NLP group at SRI for several years) remembers that for many who worked with her at SRI, “Jane was like a second mother, loving and giving and nurturing.” Jerry Hobbs (ACL president, 1990) recalls that, “one of the things I learned from her, though imperfectly, was how to be tough with grace.” Several remember Jane as one of a handful of elder statesmen that their generation could look up to.Jane was a colleague and friend of Ray Perrault (ACL president, 1983, and current AIC director) from the time he was a Ph.D. student at the University of Michigan. In the early 1970s, Jane frequently visited Joyce Friedman (ACL president, 1971), her old friend and his advisor. Ray remembers Jane was a gentle but firm critic of his thesis, and he fondly recalls her passing through in her huge Chrysler on her move from IBM to SRI. Ray was ACL vice president when Jane was president, and she was delighted when he decided to join the young Turks at SRI. Subsequently, she “even tolerated me as her manager until her retirement and became doting godmother to my son.”Jane made a difference in people's lives, not just their research. Her death marks the end of an era and the passing of an icon. We will miss her greatly.This remembrance was a composite of the reminiscences of a number of people, including Tom Garvey, Marguerite Hays, Peter Hart, Gary Hendrix, Jerry Hobbs, David Israel, Ray Perrault, Candy Sidner, and Marty Tenenbaum.

This paper defines the "Single Agent in a Team Decision" (SATD) problem. SATD differs from prior multi-agent communication problems in the assumptions it makes about teammates' knowledge of each other's plans and possible observations. The paper proposes a novel integrated logical-decision-theoretic approach to solving SATD problems, called MDP-PRT. Evaluation of MDP-PRT shows that it outperforms a previously proposed communication mechanism that did not consider the timing of communication and compares favorably with a coordinated Dec-POMDP solution that uses knowledge about all possible observations.

When interacting with computer agents, people make inferences about various characteristics of these agents, such as their reliability and trustworthiness. These perceptions are significant, as they influence people's behavior towards the agents, and may foster or inhibit repeated interactions between them. In this paper we investigate whether computer agents can use the expression of emotion to influence human perceptions of trustworthiness. In particular, we study human-computer interactions within the context of a negotiation game, in which players make alternating offers to decide on how to divide a set of resources. A series of negotiation games between a human and several agents is then followed by a "trust game." In this game people have to choose one among several agents to interact with, as well as how much of their resources they will trust to it. Our results indicate that, among those agents that displayed emotion, those whose expression was in accord with their actions (strategy) during the negotiation game were generally preferred as partners in the trust game over those whose emotion expressions and actions did not mesh. Moreover, we observed that when emotion does not carry useful new information, it fails to strongly influence human decision-making behavior in a negotiation setting.

In traditional human-computer interfaces, a human master directs a computer system as a servant, telling it not only what to do, but also how to do it. Collaborative interfaces attempt to realign the roles, making the participants collaborators in solving the person's problem. This paper describes Writer's Aid, a system that deploys AI planning techniques to enable it to serve as an author's collaborative assistant. Writer's Aid differs from previous collaborative interfaces in both the kinds of actions the system partner takes and the underlying technology it uses to do so. While an author writes a document, Writer's Aid helps in identifying and inserting citation keys and by autonomously finding and caching potentially relevant papers and their associated bibliographic information from various on-line sources. This autonomy, enabled by the use of a planning system at the core of Writer's Aid, distinguishes this system from other collaborative interfaces. The collaborative design and its division of labor result in more efficient operation: faster and easier writing on the user's part and more effective information gathering on the part of the system. Subjects in our laboratory user study found the system effective and the interface intuitive and easy to use.

This paper reports on a study of the relationship between acoustic-prosodic variation and discourse structure, as determined from an independent model of discourse. We present results of two pilot studies. Our corpus consisted of three AP news stories recorded by a professional speaker. Discourse structure was labeled by subjects either from text alone or from text (with all orthographic markings except sentence-final punctuation removed) and speech, following Grosz & Sidner 1986; average inter-labeler agreement for structural elements varied from 74.3%-95.1%, depending upon feature. These elements of global structure, together with elements of local structure such as parentheticals and attributive tags, were correlated with variation in into- This research was partly supported by NSF grant #IRI-9009018. national and acoustic features such as pitch range, contour, timing, and amplitude. We found statistically significant associations between aspects of pitch range, amplitude, and t...

Linguistic theories typically assign various linguistic phenomena to one of the categories, syntactic, semantic, or pragmatic, as if the phenomena in each category were relatively independent of those in the others. However, various phenomena in discourse do not seem to yield comfortably to any account that is strictly a syntactic or semantic or pragmatic one. This paper focuses on particular phenomena of this sort-the use of various referring expressions such as definite noun phrases and pronouns-and examines their interaction with mechanisms used to maintain discourse coherence.

Article Free Access Share on Transportable natural-language interfaces: problems and techniques Author: Barbara J. Grosz SRI International, Menlo Park, CA SRI International, Menlo Park, CAView Profile Authors Info & Claims ACL '82: Proceedings of the 20th annual meeting on Association for Computational LinguisticsJune 1982 Pages 46–50https://doi.org/10.3115/981251.981262Online:16 June 1982Publication History 3citation155DownloadsMetricsTotal Citations3Total Downloads155Last 12 Months4Last 6 weeks1 Get Citation AlertsNew Citation Alert added!This alert has been successfully added and will be sent to:You will be notified whenever a record that you have chosen has been cited.To manage your alert preferences, click on the button below.Manage my AlertsNew Citation Alert!Please log in to your account Save to BinderSave to BinderCreate a New BinderNameCancelCreateExport CitationPublisher SiteeReaderPDF

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