Analysing the potential of seq-to-seq models for incremental interpretation in task-oriented dialogue

We investigate how encoder-decoder models trained on a synthetic dataset of task-oriented dialogues process disfluencies, such as hesitations and self-corrections. We find that, contrary to earlier results, disfluencies have very little impact on the task success of seq-to-seq models with attention. Using visualisation and diagnostic classifiers, we analyse the representations that are incrementally built by the model, and discover that models develop little to no awareness of the structure of disfluencies. However, adding disfluencies to the data appears to help the model create clearer representations overall, as evidenced by the attention patterns the different models exhibit.Comment: accepted to the EMNLP2018 workshop "Analyzing and interpreting neural networks for NLP

End-to-End Goal-Oriented Conversational Agent for Risk Awareness

Traditional development of goal-oriented conversational agents typically require a lot of domain-specific handcrafting, which precludes scaling up to different domains; end-to-end systems would escape this limitation because they can be trained directly from dialogues. The very promising success recently obtained in end-to-end chatbots development could carry over to goal-oriented settings: applying deep learning models for building robust and scalable goal-oriented dialog systems directly from corpora of conversations is a challenging task and an open research area. For this reason, I decided that it would have been more relevant in the context of a master's thesis to experiment and get acquainted with these new promising methodologies - although not yet ready for production - rather than investing time in hand-crafting dialogue rules for a domain-specific solution. My thesis work had the following macro objectives: (i) investigate the latest research works concerning goal-oriented conversational agents development; (ii) choose a reference study, understand it and implement it with an appropriate technology; (iii) apply what learnt to a particular domain of interest. As a reference framework I chose the end-to-end memory networks (MemN2N) (Sukhbaatar et al., 2015) because it has proven to be particularly promising and has been used as a baseline for many recent works. Not having real dialogues available for training though, I took care of synthetically generating a corpora of conversations, taking a cue from the Dialog bAbI dataset for restaurant reservations (Bordes et al., 2016) and adapting it to the new domain of interest of risk awareness. Finally, I built a simple prototype which exploited the pre-trained dialog model in order to advise users about risk through an anthropomorphic talking avatar interface

Incremental Composition in Distributional Semantics

Despite the incremental nature of Dynamic Syntax (DS), the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal

Incremental Composition in Distributional Semantics

Despite the incremental nature of Dynamic Syntax (DS), the semantic grounding of it remains that of predicate logic, itself grounded in set theory, so is poorly suited to expressing the rampantly context-relative nature of word meaning, and related phenomena such as incremental judgements of similarity needed for the modelling of disambiguation. Here, we show how DS can be assigned a compositional distributional semantics which enables such judgements and makes it possible to incrementally disambiguate language constructs using vector space semantics. Building on a proposal in our previous work, we implement and evaluate our model on real data, showing that it outperforms a commonly used additive baseline. In conclusion, we argue that these results set the ground for an account of the non-determinism of lexical content, in which the nature of word meaning is its dependence on surrounding context for its construal

A rule triggering system for automatic text-to-sign translation

International audienceThe topic of this paper is machine translation (MT) from French text to French Sign Language (LSF). After arguing in favour of a rule-based method, it presents the architecture of an original MT system, built on two distinct efforts: formalising LSF production rules and triggering them with text processing. The former is made without any concern for text or translation and involves corpus analysis to link LSF form features to linguistic functions. It produces a set of production rules which may constitute a full LSF production grammar. The latter is an information extraction task from text, broken down in as many subtasks as there are rules in the grammar. After discussing this architecture, comparing it to the traditional methods and presenting the methodology for each task, the paper present the set of production rules found to govern event precedence and duration in LSF, and gives a progress report on the implementation of the rule triggering system. With this proposal, it is also hoped to show how MT can benefit today from Sign Language processing

Development of Cognitive Capabilities in Humanoid Robots

Merged with duplicate record 10026.1/645 on 03.04.2017 by CS (TIS)Building intelligent systems with human level of competence is the ultimate grand challenge for science and technology in general, and especially for the computational intelligence community. Recent theories in autonomous cognitive systems have focused on the close integration (grounding) of communication with perception, categorisation and action. Cognitive systems are essential for integrated multi-platform systems that are capable of sensing and communicating. This thesis presents a cognitive system for a humanoid robot that integrates abilities such as object detection and recognition, which are merged with natural language understanding and refined motor controls. The work includes three studies; (1) the use of generic manipulation of objects using the NMFT algorithm, by successfully testing the extension of the NMFT to control robot behaviour; (2) a study of the development of a robotic simulator; (3) robotic simulation experiments showing that a humanoid robot is able to acquire complex behavioural, cognitive, and linguistic skills through individual and social learning. The robot is able to learn to handle and manipulate objects autonomously, to cooperate with human users, and to adapt its abilities to changes in internal and environmental conditions. The model and the experimental results reported in this thesis, emphasise the importance of embodied cognition, i.e. the humanoid robot's physical interaction between its body and the environment

A Computational Model of Non-Cooperation in Natural Language Dialogue

A common assumption in the study of conversation is that participants fully cooperate in order to maximise the effectiveness of the exchange and ensure communication flow. This assumption persists even in situations in which the private goals of the participants are at odds: they may act strategically pursuing their agendas, but will still adhere to a number of linguistic norms or conventions which are implicitly accepted by a community of language users. However, in naturally occurring dialogue participants often depart from such norms, for instance, by asking inappropriate questions, by avoiding to provide adequate answers or by volunteering information that is not relevant to the conversation. These are examples of what we call linguistic non-cooperation. This thesis presents a systematic investigation of linguistic non-cooperation in dialogue. Given a specific activity, in a specific cultural context and time, the method proceeds by making explicit which linguistic behaviours are appropriate. This results in a set of rules: the global dialogue game. Non-cooperation is then measured as instances in which the actions of the participants are not in accordance with these rules. The dialogue game is formally defined in terms of discourse obligations. These are actions that participants are expected to perform at a given point in the dialogue based on the dialogue history. In this context, non-cooperation amounts to participants failing to act according to their obligations. We propose a general definition of linguistic non-cooperation and give a specific instance for political interview dialogues. Based on the latter, we present an empirical method which involves a coding scheme for the manual annotation of interview transcripts. The degree to which each participant cooperates is automatically determined by contrasting the annotated transcripts with the rules in the dialogue game for political interviews. The approach is evaluated on a corpus of broadcast political interviews and tested for correlation with human judgement on the same corpus. Further, we describe a model of conversational agents that incorporates the concepts and mechanisms above as part of their dialogue manager. This allows for the generation of conversations in which the agents exhibit varying degrees of cooperation by controlling how often they favour their private goals instead of discharging their discourse obligations

Relocating Transitional Justice from International Law to Muslim-majority Legal Systems: Concepts, Approaches and Ways Forward

Faced with the constant challenge of adapting to different contexts, the current understanding of transitional justice held by worldwide institutions, NGOs, donors and successor administrations cannot rely on international law alone as a framework of reference for the design and implementation of transitional processes - although the identification, interpretation and uses of local norms is inherently problematic. This thesis considers the tension between different rules applicable to transitional justice and explores their coexistence in the context of legal pluralism, drawing on comparative law perspectives to investigate the distinctive concept of legal truth and the victims’ right to it, within the broader transitional aims of accountability, justice and reconciliation after a history of serious abuse. The particular focus on Muslim-majority legal systems provides further appreciation of how transitional justice can be relocated from international law to a given local setting, discussing the difficulties in doing so and the possible solutions with reference to Islamic law and jurisprudence. Rejecting the universalist v relativist deadlock in favour of an interpretation of international law which is permeable to local practices (also channeled through states), this thesis argues that comparative law can help uncover the legal formants of a system and piece together a global set of rules for transitional justice which rely on different normative provenances. Based on a victim-centred approach to transitional justice and the acknowledgement of structural power struggles within societies facing radical political change, this work argues that local and global norms of transitional justice have the potential to cross-fertilise in delivering the key transitional aims. Cultural ownership of rules should not be limited to international actors, national or community leaders: if local unofficial norms resonate with victims and survivors of abuse, provided they do not contrast the transitional objectives, they are likely to contribute to given processes, and in turn influence the global paradigm of transitional justice