Julian Hough


2021

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Communicative Grounding of Analogical Explanations in Dialogue: A Corpus Study of Conversational Management Acts and Statistical Sequence Models for Tutoring through Analogy
Jorge Del-Bosque-Trevino | Julian Hough | Matthew Purver
Proceedings of the Reasoning and Interaction Conference (ReInAct 2021)

We present a conversational management act (CMA) annotation schema for one-to-one tutorial dialogue sessions where a tutor uses an analogy to teach a student a concept. CMAs are more fine-grained sub-utterance acts compared to traditional dialogue act mark-up. The schema achieves an inter-annotator agreement (IAA) Cohen Kappa score of at least 0.66 across all 10 classes. We annotate a corpus of analogical episodes with the schema and develop statistical sequence models from the corpus which predict tutor content related decisions, in terms of the selection of the analogical component (AC) and tutor conversational management act (TCMA) to deploy at the current utterance, given the student’s behaviour. CRF sequence classifiers perform well on AC selection and robustly on TCMA selection, achieving respective accuracies of 61.9% and 56.3% on a cross-validation experiment over the corpus.

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Rare-Class Dialogue Act Tagging for Alzheimer’s Disease Diagnosis
Shamila Nasreen | Julian Hough | Matthew Purver
Proceedings of the 22nd Annual Meeting of the Special Interest Group on Discourse and Dialogue

Alzheimer’s Disease (AD) is associated with many characteristic changes, not only in an individual’s language but also in the interactive patterns observed in dialogue. The most indicative changes of this latter kind tend to be associated with relatively rare dialogue acts (DAs), such as those involved in clarification exchanges and responses to particular kinds of questions. However, most existing work in DA tagging focuses on improving average performance, effectively prioritizing more frequent classes; it thus gives a poor performance on these rarer classes and is not suited for application to AD analysis. In this paper, we investigate tagging specifically for rare class DAs, using a hierarchical BiLSTM model with various ways of incorporating information from previous utterances and DA tags in context. We show that this can give good performance for rare DA classes on both the general Switchboard corpus (SwDA) and an AD-specific conversational dataset, the Carolinas Conversation Collection (CCC); and that the tagger outputs then contribute useful information for distinguishing patients with and without AD

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Best of Both Worlds: Making High Accuracy Non-incremental Transformer-based Disfluency Detection Incremental
Morteza Rohanian | Julian Hough
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

While Transformer-based text classifiers pre-trained on large volumes of text have yielded significant improvements on a wide range of computational linguistics tasks, their implementations have been unsuitable for live incremental processing thus far, operating only on the level of complete sentence inputs. We address the challenge of introducing methods for word-by-word left-to-right incremental processing to Transformers such as BERT, models without an intrinsic sense of linear order. We modify the training method and live decoding of non-incremental models to detect speech disfluencies with minimum latency and without pre-segmentation of dialogue acts. We experiment with several decoding methods to predict the rightward context of the word currently being processed using a GPT-2 language model and apply a BERT-based disfluency detector to sequences, including predicted words. We show our method of incrementalising Transformers maintains most of their high non-incremental performance while operating strictly incrementally. We also evaluate our models’ incremental performance to establish the trade-off between incremental performance and final performance, using different prediction strategies. We apply our system to incremental speech recognition results as they arrive into a live system and achieve state-of-the-art results in this setting.

2020

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Re-framing Incremental Deep Language Models for Dialogue Processing with Multi-task Learning
Morteza Rohanian | Julian Hough
Proceedings of the 28th International Conference on Computational Linguistics

We present a multi-task learning framework to enable the training of one universal incremental dialogue processing model with four tasks of disfluency detection, language modelling, part-of-speech tagging and utterance segmentation in a simple deep recurrent setting. We show that these tasks provide positive inductive biases to each other with optimal contribution of each one relying on the severity of the noise from the task. Our live multi-task model outperforms similar individual tasks, delivers competitive performance and is beneficial for future use in conversational agents in psychiatric treatment.

2019

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Proceedings of the IWCS Workshop Vector Semantics for Discourse and Dialogue
Mehrnoosh Sadrzadeh | Matthew Purver | Arash Eshghi | Julian Hough | Ruth Kempson | Patrick G. T. Healey
Proceedings of the IWCS Workshop Vector Semantics for Discourse and Dialogue

2017

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Natural Language Informs the Interpretation of Iconic Gestures: A Computational Approach
Ting Han | Julian Hough | David Schlangen
Proceedings of the Eighth International Joint Conference on Natural Language Processing (Volume 2: Short Papers)

When giving descriptions, speakers often signify object shape or size with hand gestures. Such so-called ‘iconic’ gestures represent their meaning through their relevance to referents in the verbal content, rather than having a conventional form. The gesture form on its own is often ambiguous, and the aspect of the referent that it highlights is constrained by what the language makes salient. We show how the verbal content guides gesture interpretation through a computational model that frames the task as a multi-label classification task that maps multimodal utterances to semantic categories, using annotated human-human data.

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Joint, Incremental Disfluency Detection and Utterance Segmentation from Speech
Julian Hough | David Schlangen
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

We present the joint task of incremental disfluency detection and utterance segmentation and a simple deep learning system which performs it on transcripts and ASR results. We show how the constraints of the two tasks interact. Our joint-task system outperforms the equivalent individual task systems, provides competitive results and is suitable for future use in conversation agents in the psychiatric domain.

2016

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Investigating Fluidity for Human-Robot Interaction with Real-time, Real-world Grounding Strategies
Julian Hough | David Schlangen
Proceedings of the 17th Annual Meeting of the Special Interest Group on Discourse and Dialogue

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PentoRef: A Corpus of Spoken References in Task-oriented Dialogues
Sina Zarrieß | Julian Hough | Casey Kennington | Ramesh Manuvinakurike | David DeVault | Raquel Fernández | David Schlangen
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

PentoRef is a corpus of task-oriented dialogues collected in systematically manipulated settings. The corpus is multilingual, with English and German sections, and overall comprises more than 20000 utterances. The dialogues are fully transcribed and annotated with referring expressions mapped to objects in corresponding visual scenes, which makes the corpus a rich resource for research on spoken referring expressions in generation and resolution. The corpus includes several sub-corpora that correspond to different dialogue situations where parameters related to interactivity, visual access, and verbal channel have been manipulated in systematic ways. The corpus thus lends itself to very targeted studies of reference in spontaneous dialogue.

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DUEL: A Multi-lingual Multimodal Dialogue Corpus for Disfluency, Exclamations and Laughter
Julian Hough | Ye Tian | Laura de Ruiter | Simon Betz | Spyros Kousidis | David Schlangen | Jonathan Ginzburg
Proceedings of the Tenth International Conference on Language Resources and Evaluation (LREC'16)

We present the DUEL corpus, consisting of 24 hours of natural, face-to-face, loosely task-directed dialogue in German, French and Mandarin Chinese. The corpus is uniquely positioned as a cross-linguistic, multimodal dialogue resource controlled for domain. DUEL includes audio, video and body tracking data and is transcribed and annotated for disfluency, laughter and exclamations.

2015

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Incremental Semantics for Dialogue Processing: Requirements, and a Comparison of Two Approaches
Julian Hough | Casey Kennington | David Schlangen | Jonathan Ginzburg
Proceedings of the 11th International Conference on Computational Semantics

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Feedback in Conversation as Incremental Semantic Update
Arash Eshghi | Christine Howes | Eleni Gregoromichelaki | Julian Hough | Matthew Purver
Proceedings of the 11th International Conference on Computational Semantics

2014

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Strongly Incremental Repair Detection
Julian Hough | Matthew Purver
Proceedings of the 2014 Conference on Empirical Methods in Natural Language Processing (EMNLP)

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Probabilistic Type Theory for Incremental Dialogue Processing
Julian Hough | Matthew Purver
Proceedings of the EACL 2014 Workshop on Type Theory and Natural Language Semantics (TTNLS)

2013

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Probabilistic induction for an incremental semantic grammar
Arash Eshghi | Matthew Purver | Julian Hough
Proceedings of the 10th International Conference on Computational Semantics (IWCS 2013) – Long Papers

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Incremental Grammar Induction from Child-Directed Dialogue Utterances
Arash Eshghi | Julian Hough | Matthew Purver
Proceedings of the Fourth Annual Workshop on Cognitive Modeling and Computational Linguistics (CMCL)

2011

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Incremental Semantic Construction in a Dialogue System
Matthew Purver | Arash Eshghi | Julian Hough
Proceedings of the Ninth International Conference on Computational Semantics (IWCS 2011)

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Incremental Semantics Driven Natural Language Generation with Self-Repairing Capability
Julian Hough
Proceedings of the Second Student Research Workshop associated with RANLP 2011