Nicola Horst, Davide Mazzaccara, Antonia Schmidt, Michael Sullivan, Filippo Momentè, Luca Franceschetti, Philipp Sadler, Sherzod Hakimov, Alberto Testoni, Raffaella Bernardi, Raquel Fernández, Alexander Koller, Oliver Lemon, David Schlangen, Mario Giulianelli, Alessandro Suglia. Proceedings of the 2025 Conference on Empirical Methods in Natural Language Processing. 2025.

Generative Large Language Models (LLMs) infer user's demographic information from subtle cues in the conversation -a phenomenon called implicit personalization.Prior work has shown that such inferences can lead to lower quality responses for users assumed to be from minority groups, even when no demographic information is explicitly provided.In this work, we systematically explore how LLMs respond to stereotypical cues using controlled synthetic conversations, by analyzing the models' latent user representations through both model internals and generated answers to targeted user questions.Our findings reveal that LLMs do infer demographic attributes based on these stereotypical signals, which for a number of groups even persists when the user explicitly identifies with a different demographic group.Finally, we show that this form of stereotypedriven implicit personalization can be effectively mitigated by intervening on the model's internal representations using a trained linear probe to steer them toward the explicitly stated identity.Our results highlight the need for greater transparency and control in how LLMs represent user identity.

Ambiguity resolution is key to effective communication.While humans effortlessly address ambiguity through conversational grounding strategies, the extent to which current language models can emulate these strategies remains unclear.In this work, we examine referential ambiguity in image-based question answering by introducing RACQUET, a carefully curated dataset targeting distinct aspects of ambiguity.Through a series of evaluations, we reveal significant limitations and problems of overconfidence of state-of-the-art large multimodal language models in addressing ambiguity in their responses.The overconfidence issue becomes particularly relevant for RACQUET-BIAS, a subset designed to analyze a critical yet underexplored problem: failing to address ambiguity leads to stereotypical, socially biased responses.Our results underscore the urgency of equipping models with robust strategies to deal with uncertainty without resorting to undesirable stereotypes.

There is an intricate relation between the properties of an image and how humans behave while describing the image.This behavior shows ample variation, as manifested in human signals such as eye movements and when humans start to describe the image.Despite the value of such signals of visuo-linguistic variation, they are virtually disregarded in the training of current pretrained models, which motivates further investigation.Using a corpus of Dutch image descriptions with concurrently collected eye-tracking data, we explore the nature of the variation in visuo-linguistic signals, and find that they correlate with each other.Given this result, we hypothesize that variation stems partly from the properties of the images, and explore whether image representations encoded by pretrained vision encoders can capture such variation.Our results indicate that pretrained models do so to a weak-to-moderate degree, suggesting that the models lack biases about what makes a stimulus complex for humans and what leads to variations in human outputs.

Clarification questions are an essential dialogue tool to signal misunderstanding, ambiguities, and under-specification in language use.While humans are able to resolve uncertainty by asking questions since childhood, modern dialogue systems struggle to generate effective questions.To make progress in this direction, in this work we take a collaborative dialogue task as a testbed and study how model uncertainty relates to human uncertainty-an as yet underexplored problem.We show that model uncertainty does not mirror human clarificationseeking behavior, which suggests that using human clarification questions as supervision for deciding when to ask may not be the most effective way to resolve model uncertainty.To address this issue, we propose an approach to generating clarification questions based on model uncertainty estimation, compare it to several alternatives, and show that it leads to significant improvements in terms of task success.Our findings highlight the importance of equipping dialogue systems with the ability to assess their own uncertainty and exploit in interaction.

Ensuring the verifiability of model answers is a fundamental challenge for retrieval-augmented generation (RAG) in the question answering (QA) domain.Recently, self-citation prompting was proposed to make large language models (LLMs) generate citations to supporting documents along with their answers.However, self-citing LLMs often struggle to match the required format, refer to non-existent sources, and fail to faithfully reflect LLMs' context usage throughout the generation.In this work, we present MIRAGE -Model Internals-based RAG Explanations -a plug-and-play approach using model internals for faithful answer attribution in RAG applications.MIRAGE detects context-sensitive answer tokens and pairs them with retrieved documents contributing to their prediction via saliency methods.We evaluate our proposed approach on a multilingual extractive QA dataset, finding high agreement with human answer attribution.On open-ended QA, MIRAGE achieves citation quality and efficiency comparable to self-citation while also allowing for a finer-grained control of attribution parameters.Our qualitative evaluation highlights the faithfulness of MIRAGE's attributions and underscores the promising application of model internals for RAG answer attribution. 1

In face-to-face dialogues, the form-meaning relationship of co-speech gestures varies depending on contextual factors such as what the gestures refer to and the individual characteristics of speakers. These factors make co-speech gesture representation learning challenging. How can we learn meaningful gestures representations considering gestures’ variability and relationship with speech? This paper tackles this challenge by employing self-supervised contrastive learning techniques to learn gesture representations from skeletal and speech information. We propose an approach that includes both unimodal and multimodal pre-training to ground gesture representations in co-occurring speech. For training, we utilize a face-to-face dialogue dataset rich with representational iconic gestures. We conduct thorough intrinsic evaluations of the learned representations through comparison with human-annotated pairwise gesture similarity. Moreover, we perform a diagnostic probing analysis to assess the possibility of recovering interpretable gesture features from the learned representations. Our results show a significant positive correlation with human-annotated gesture similarity and reveal that the similarity between the learned representations is consistent with well-motivated patterns related to the dynamics of dialogue interaction. Moreover, our findings demonstrate that several features concerning the form of gestures can be recovered from the latent representations. Overall, this study shows that multimodal contrastive learning is a promising approach for learning gesture representations, which opens the door to using such representations in larger-scale gesture analysis studies.

Gestures are integral components of face-to-face communication. They unfold over time, often following predictable movement phases of preparation, stroke, and retraction. Yet, the prevalent approach to automatic gesture detection treats the problem as binary classification, classifying a segment as either containing a gesture or not, thus failing to capture its inherently sequential and contextual nature. To address this, we introduce a novel framework that reframes the task as a multi-phase sequence labeling problem rather than binary classification. Our model processes sequences of skeletal movements over time windows, uses Transformer encoders to learn contextual embeddings, and leverages Conditional Random Fields to perform sequence labeling. We evaluate our proposal on a large dataset of diverse co-speech gestures in task-oriented face-to-face dialogues. The results consistently demonstrate that our method significantly outperforms strong baseline models in detecting gesture strokes. Furthermore, applying Transformer encoders to learn contextual embeddings from movement sequences substantially improves gesture unit detection. These results highlight our framework’s capacity to capture the fine-grained dynamics of co-speech gesture phases, paving the way for more nuanced and accurate gesture detection and analysis.

We propose using automatically generated natural language definitions of contextualised word usages as interpretable word and word sense representations.Given a collection of usage examples for a target word, and the corresponding data-driven usage clusters (i.e., word senses), a definition is generated for each usage with a specialised Flan-T5 language model, and the most prototypical definition in a usage cluster is chosen as the sense label.We demonstrate how the resulting sense labels can make existing approaches to semantic change analysis more interpretable, and how they can allow users-historical linguists, lexicographers, or social scientists-to explore and intuitively explain diachronic trajectories of word meaning.Semantic change analysis is only one of many possible applications of the 'definitions as representations' paradigm.Beyond being human-readable, contextualised definitions also outperform token or usage sentence embeddings in word-in-context semantic similarity judgements, making them a new promising type of lexical representation for NLP.

Despite the impressive performance achieved by pre-trained language-and-vision models in downstream tasks, it remains an open question whether this reflects a proper understanding of image-text interaction.In this work, we explore to what extent they handle basic linguistic constructions-active-passive voice, coordination, and relative clauses-that even preschool children can typically master.We present BLA, a novel, automatically constructed benchmark to evaluate multimodal models on these Basic Language Abilities.We show that different types of Transformer-based systems, such as CLIP, ViLBERT, and BLIP2, generally struggle with BLA in a zero-shot setting, in line with previous findings.Our experiments, in particular, show that most of the tested models only marginally benefit when fine-tuned or prompted with construction-specific samples.Yet, the generative BLIP2 shows promising trends, especially in an in-context learning setting.This opens the door to using BLA not only as an evaluation benchmark but also to improve models' basic language abilities.

In Natural Language Generation (NLG) tasks, for any input, multiple communicative goals are plausible, and any goal can be put into words, or produced, in multiple ways.We characterise the extent to which human production varies lexically, syntactically, and semantically across four NLG tasks, connecting human production variability to aleatoric or data uncertainty.We then inspect the space of output strings shaped by a generation system's predicted probability distribution and decoding algorithm to probe its uncertainty.For each test input, we measure the generator's calibration to human production variability.Following this instance-level approach, we analyse NLG models and decoding strategies, demonstrating that probing a generator with multiple samples and, when possible, multiple references, provides the level of detail necessary to gain understanding of a model's representation of uncertainty. 1 * Equal contribution. 1 https://github.com/dmg-illc/nlg-uncertainty-probes

We present information value, a measure which quantifies the predictability of an utterance relative to a set of plausible alternatives.We introduce a method to obtain interpretable estimates of information value using neural text generators, and exploit their psychometric predictive power to investigate the dimensions of predictability that drive human comprehension behaviour.Information value is a stronger predictor of utterance acceptability in written and spoken dialogue than aggregates of token-level surprisal and it is complementary to surprisal for predicting eye-tracked reading times. 1

Multilingual large-scale Pretrained Language Models (PLMs) have been shown to store considerable amounts of factual knowledge, but large variations are observed across languages.With the ultimate goal of ensuring that users with different language backgrounds obtain consistent feedback from the same model, we study the cross-lingual consistency (CLC) of factual knowledge in various multilingual PLMs.To this end, we propose a Rankingbased Consistency (RankC) metric to evaluate knowledge consistency across languages independently from accuracy.Using this metric, we conduct an in-depth analysis of the determining factors for CLC, both at model level and at language-pair level.Among other results, we find that increasing model size leads to higher factual probing accuracy in most languages, but does not improve cross-lingual consistency.Finally, we conduct a case study on CLC when new factual associations are inserted in the PLMs via model editing.Results on a small sample of facts inserted in English reveal a clear pattern whereby the new piece of knowledge transfers only to languages with which English has a high RankC score. 1

A proper evaluation of stories generated for a sequence of images-the task commonly referred to as visual storytelling-must consider multiple aspects, such as coherence, grammatical correctness, and visual grounding.In this work, we focus on evaluating the degree of grounding, that is, the extent to which a story is about the entities shown in the images.We analyze current metrics, both designed for this purpose and for general vision-text alignment.Given their observed shortcomings, we propose a novel evaluation tool, GROOViST, that accounts for cross-modal dependencies, temporal misalignments (the fact that the order in which entities appear in the story and the image sequence may not match), and human intuitions on visual grounding.An additional advantage of GROOViST is its modular design, where the contribution of each component can be assessed and interpreted individually.

Calibration is a popular framework to evaluate whether a classifier knows when it does not know-i.e., its predictive probabilities are a good indication of how likely a prediction is to be correct.Correctness is commonly estimated against the human majority class.Recently, calibration to human majority has been measured on tasks where humans inherently disagree about which class applies.We show that measuring calibration to human majority given inherent disagreements is theoretically problematic, demonstrate this empirically on the ChaosNLI dataset, and derive several instancelevel measures of calibration that capture key statistical properties of human judgementsclass frequency, ranking and entropy. 1

Abstract We investigate the extent to which modern neural language models are susceptible to structural priming, the phenomenon whereby the structure of a sentence makes the same structure more probable in a follow-up sentence. We explore how priming can be used to study the potential of these models to learn abstract structural information, which is a prerequisite for good performance on tasks that require natural language understanding skills. We introduce a novel metric and release Prime-LM, a large corpus where we control for various linguistic factors that interact with priming strength. We find that Transformer models indeed show evidence of structural priming, but also that the generalizations they learned are to some extent modulated by semantic information. Our experiments also show that the representations acquired by the models may not only encode abstract sequential structure but involve certain level of hierarchical syntactic information. More generally, our study shows that the priming paradigm is a useful, additional tool for gaining insights into the capacities of language models and opens the door to future priming-based investigations that probe the model’s internal states.1

Speakers are thought to use rational information transmission strategies for efficient communication (Genzel and Charniak, 2002;Aylett and Turk, 2004;Jaeger and Levy, 2007).Previous work analysing these strategies in sentence production has failed to take into account how the information content of sentences varies as a function of the available discourse context.In this study, we estimate sentence information content within discourse context.We find that speakers transmit information at a stable rate-i.e., rationally-in English newspaper articles but that this rate decreases in spoken open domain and written task-oriented dialogues.We also observe that speakers' choices are not oriented towards local uniformity of information, which is another hypothesised rational strategy.We suggest that a more faithful model of communication should explicitly include production costs and goal-oriented rewards.

The Uniform Information Density principle states that speakers plan their utterances to reduce fluctuations in the density of the information transmitted.In this paper, we test whether, and within which contextual units this principle holds in task-oriented dialogues.We show that there is evidence supporting the principle in written dialogues where participants play a cooperative reference game as well as in spoken dialogues involving instruction giving and following.Our study underlines the importance of identifying the relevant contextual components, showing that information content increases particularly within topically and referentially related contextual units.

Abstract This study carries out a systematic intrinsic evaluation of the semantic representations learned by state-of-the-art pre-trained multimodal Transformers. These representations are claimed to be task-agnostic and shown to help on many downstream language-and-vision tasks. However, the extent to which they align with human semantic intuitions remains unclear. We experiment with various models and obtain static word representations from the contextualized ones they learn. We then evaluate them against the semantic judgments provided by human speakers. In line with previous evidence, we observe a generalized advantage of multimodal representations over language- only ones on concrete word pairs, but not on abstract ones. On the one hand, this confirms the effectiveness of these models to align language and vision, which results in better semantic representations for concepts that are grounded in images. On the other hand, models are shown to follow different representation learning patterns, which sheds some light on how and when they perform multimodal integration.

This paper presents the first unsupervised approach to lexical semantic change that makes use of contextualised word representations.We propose a novel method that exploits the BERT neural language model to obtain representations of word usages, clusters these representations into usage types, and measures change along time with three proposed metrics.We create a new evaluation dataset and show that the model representations and the detected semantic shifts are positively correlated with human judgements.Our extensive qualitative analysis demonstrates that our method captures a variety of synchronic and diachronic linguistic phenomena.We expect our work to inspire further research in this direction.

Cognitive and social traits of individuals are reflected in language use.Moreover, individuals who are prone to spread fake news online often share common traits.Building on these ideas, we introduce a model that creates representations of individuals on social media based only on the language they produce, and use them to detect fake news.We show that language-based user representations are beneficial for this task.We also present an extended analysis of the language of fake news spreaders, showing that its main features are mostly domain independent and consistent across two English datasets.Finally, we exploit the relation between language use and connections in the social graph to assess the presence of the Echo Chamber effect in our data.

Dialogue participants often refer to entities or situations repeatedly within a conversation, which contributes to its cohesiveness.Subsequent references exploit the common ground accumulated by the interlocutors and hence have several interesting properties, namely, they tend to be shorter and reuse expressions that were effective in previous mentions.In this paper, we tackle the generation of first and subsequent references in visually grounded dialogue.We propose a generation model that produces referring utterances grounded in both the visual and the conversational context.To assess the referring effectiveness of its output, we also implement a reference resolution system.Our experiments and analyses show that the model produces better, more effective referring utterances than a model not grounded in the dialogue context, and generates subsequent references that exhibit linguistic patterns akin to humans.

When speakers describe an image, they tend to look at objects before mentioning them.In this paper, we investigate such sequential crossmodal alignment by modelling the image description generation process computationally.We take as our starting point a state-of-theart image captioning system and develop several model variants that exploit information from human gaze patterns recorded during language production.In particular, we propose the first approach to image description generation where visual processing is modelled sequentially.Our experiments and analyses confirm that better descriptions can be obtained by exploiting gaze-driven attention and shed light on human cognitive processes by comparing different ways of aligning the gaze modality with language production.We find that processing gaze data sequentially leads to descriptions that are better aligned to those produced by speakers, more diverse, and more naturalparticularly when gaze is encoded with a dedicated recurrent component.

We study the issue of catastrophic forgetting in the context of neural multimodal approaches to Visual Question Answering (VQA).Motivated by evidence from psycholinguistics, we devise a set of linguistically-informed VQA tasks, which differ by the types of questions involved (Wh-questions and polar questions).We test what impact task difficulty has on continual learning, and whether the order in which a child acquires question types facilitates computational models.Our results show that dramatic forgetting is at play and that task difficulty and order matter.Two well-known current continual learning methods mitigate the problem only to a limiting degree.

This paper introduces the PhotoBook dataset, a large-scale collection of visually-grounded, task-oriented dialogues in English designed to investigate shared dialogue history accumulating during conversation.Taking inspiration from seminal work on dialogue analysis, we propose a data-collection task formulated as a collaborative game prompting two online participants to refer to images utilising both their visual context as well as previously established referring expressions.We provide a detailed description of the task setup and a thorough analysis of the 2,500 dialogues collected.To further illustrate the novel features of the dataset, we propose a baseline model for reference resolution which uses a simple method to take into account shared information accumulated in a reference chain.Our results show that this information is particularly important to resolve later descriptions and underline the need to develop more sophisticated models of common ground in dialogue interaction. 1

Sandro Pezzelle, Raquel Fernández. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Marco Del Tredici, Diego Marcheggiani, Sabine Schulte im Walde, Raquel Fernández. Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). 2019.

Our goal is to explore how the abilities brought in by a dialogue manager can be included in end-to-end visually grounded conversational agents. We make initial steps towards this general goal by augmenting a task-oriented visual dialogue model with a decision-making component that decides whether to ask a follow-up question to identify a target referent in an image, or to stop the conversation to make a guess. Our analyses show that adding a decision making component produces dialogues that are less repetitive and that include fewer unnecessary questions, thus potentially leading to more efficient and less unnatural interactions.

We investigate the birth and diffusion of lexical innovations in a large dataset of online social communities. We build on sociolinguistic theories and focus on the relation between the spread of a novel term and the social role of the individuals who use it, uncovering characteristics of innovators and adopters. Finally, we perform a prediction task that allows us to anticipate whether an innovation will successfully spread within a community.

Stylistic variation is critical to render the utterances generated by conversational agents natural and engaging.In this paper, we focus on sequence-to-sequence models for open-domain dialogue response generation and propose a new method to evaluate the extent to which such models are able to generate responses that reflect different personality traits.

We investigate the potential of adversarial evaluation methods for open-domain dialogue generation systems, comparing the performance of a discriminative agent to that of humans on the same task.Our results show that the task is hard, both for automated models and humans, but that a discriminative agent can learn patterns that lead to above-chance performance.

Denis Paperno, Germán Kruszewski, Angeliki Lazaridou, Ngoc Quan Pham, Raffaella Bernardi, Sandro Pezzelle, Marco Baroni, Gemma Boleda, Raquel Fernández. Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers). 2016.

We investigate implicit corrections in the form of contrastive discourse in childadult interaction, which have been argued to contribute to language learning.In contrast to previous work in psycholinguistics, we adopt a data-driven methodology, using comparably large amounts of data and leveraging computational methods.We conduct a corpus study on the use of parental corrective feedback and show that its presence in child directed speech is associated with a reduction of child subject omission errors in English.

During conversations, participants do not always alternate turns smoothly. One cause of disturbance particularly prominent in multiparty dialogue is the presence of interruptions: interventions that prevent current speakers from finishing their turns. Previous work, mostly within the field of sociolinguistics, has suggested that the gender of the dialogue participants plays an important role in their interruptive behaviour. We investigate existing hypotheses in this respect by systematically analysing interruptions in a corpus of spoken multiparty meetings that include a minimum of two male and two female participants. We find a number of significant differences, including the fact that women are more often interrupted overall and that men interrupt more often women than other men, in particular using speech overlap to grab the floor. We do not find evidence for the hypothesis that women interrupt other women more frequently than they interrupt men.

This paper presents a data-driven investigation of phonesthemes, phonetic units said to carry meaning associations, thus challenging the traditionally assumed arbitrariness of language.Phonesthemes have received a substantial amount of attention within the cognitive science literature on sound iconicity, but nevertheless remain a controversial and understudied phenomenon.Here we employ NLP techniques to address two main questions: How can the existence of phonesthemes be tested at a large scale with quantitative methods?And how can the meaning arguably carried by a phonestheme be induced automatically from word embeddings?We develop novel methods to make progress on these fronts and compare our results to previous work, obtaining substantial improvements.

Angeliki Lazaridou, Grzegorz Chrupała, Raquel Fernández, Marco Baroni. Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies. 2016.

We study the role that logical polarity plays in determining the rejection or ac-ceptance function of an utterance in dia-logue. We develop a model inspired by re-cent work on the semantics of negation and polarity particles and test it on annota-ted data from two spoken dialogue corpo-ra: the Switchboard Corpus and the AMI Meeting Corpus. Our experiments show that taking into account the relative pola-rity of a proposal under discussion and of its response greatly helps to distinguish re-jections from acceptances in both corpora. 1

The CALO Meeting Assistant (MA) provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper presents the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, topic identification and segmentation, question-answer pair identification, action item recognition, decision extraction, and summarization.

We address the problem of identifying words and phrases that accurately capture, or contribute to, the semantic gist of decisions made in multi-party human-human meetings. We first describe our approach to modelling decision discussions in spoken meetings and then compare two approaches to extracting information from these discussions. The first one uses an opendomain semantic parser that identifies candidate phrases for decision summaries and then employs machine learning techniques to select from those candidate phrases. The second one uses categorical and sequential classifiers that exploit simple syntactic and semantic features to identify words and phrases relevant for decision summarization.

The CALO Meeting Assistant provides for distributed meeting capture, annotation, automatic transcription and semantic analysis of multiparty meetings, and is part of the larger CALO personal assistant system. This paper summarizes the CALO-MA architecture and its speech recognition and understanding components, which include real-time and offline speech transcription, dialog act segmentation and tagging, question-answer pair identification, action item recognition, decision extraction, and summarization.

We report results of an experiment on inducing communication problems in human-human dialogue.We set up a voice-only cooperative task where we manipulated one channel by replacing (in real-time, at random points) all signal with noise.Altogether around 10% of the speaker's signal was thus removed.We found an increase in clarification requests of a form that has previously been hypothesised to be used mainly for clarifying acoustic problems.We also found a correlation between the percentage of an utterance being manipulated and the use of devices for pointing out error locations.From our findings, we derive a gold-standard policy for clarification behaviour.

In this article we use well-known machine learning methods to tackle a novel task, namely the classification of non-sentential utterances (NSUs) in dialogue. We introduce a fine-grained taxonomy of NSU classes based on corpus work, and then report on the results of several machine learning experiments. First, we present a pilot study focused on one of the NSU classes in the taxonomy—bare wh-phrases or “sluices”—and explore the task of disambiguating between the different readings that sluices can convey. We then extend the approach to classify the full range of NSU classes, obtaining results of around an 87% weighted F-score. Thus our experiments show that, for the taxonomy adopted, the task of identifying the right NSU class can be successfully learned, and hence provide a very encouraging basis for the more general enterprise of fully processing NSUs.

In human-human dialogue, the allocation of turns between the participants is normally managed smoothly, without the participants paying much attention to it. In contrast, for spoken dialogue systems turn allocation is a difficult task, and often technical restrictions are introduced to simplify it. In this paper we investigate, by comparing two experimentally collected corpora of human-human task oriented dialogue, what the consequences are of imposing one particular kind of restriction, namely that of using a simplex channel managed by push-to-talk (PTT). We found, as expected, a loss of interactivity in the PTT condition (fewer, longer turns; more silences), but surprisingly, no loss of efficiency; in fact, the subjects in the PTT condition were able to finish their task in roughly the same time, while using fewer words along the way. We analyse here the differences in the interaction patterns and the interplay of 'naturalness' and efficiency as relevant factors for practical system development.

The paper considers how to scale up dialogue protocols to multilogue, settings with multiple conversationalists. We extract two benchmarks to evaluate scaled up protocols based on the long distance resolution possibilities of non-sentential utterances in dialogue and multilogue in the British National Corpus. In light of these benchmarks, we then consider three possible transformations to dialogue protocols, formulated within an issue-based approach to dialogue management. We show that one such transformation yields protocols for querying and assertion that fulfill these benchmarks.

A low computational cost method for terrestrial mobile robots that uses a laser scanner for following mobile objects and avoiding obstacles is presented. In particular, the technique has been successfully implemented in the outdoor mobile robot Auriga-/spl alpha/. The measurement data from the laser scanner and the vehicle position are used as input variables. The outputs are the new curvature and velocity of the robot in order to follow a mobile object, or track a previously recorded path and avoid any possible obstacle in its way.

The paper deals with speed control for autonomous mobile robots. First a trajectory planner attaches a speed component to the postures of a path by considering speed limitations introduced by the vehicle and by task specifications. In order to provide robustness during task execution in the real world, environment feedback is used to dynamically adjust the speed of the vehicle to the presence of unexpected obstacles, both static and mobile. This is accomplished by combining the planned speed profile with the outputs of two reactive controllers. The system has been successfully implemented within the control architecture of the RAM-2 mobile robot.