Welcome ...

... to the website of the Dialogue Modeling Group in Amsterdam!

We carry out research at the interface of computational linguistics, cognitive modelling and artificial intelligence. Our aim is to understand how we use language to communicate with each other in situated environments and how dialogue interaction shapes learning -- about the world and about language itself. These are some of the topics we work on: Semantics and pragmatics of dialogue phenomena, visually grounded language and visual reasoning, conversational agents and learning from interaction, language variation and change in communities of speakers.

News

September 2020: new work by group members available soon!

• Generating Image Descriptions via Sequential Cross-Modal Alignment Guided by Human Gaze by Ece Takmaz, Sandro Pezzelle, Lisa Beinborn and Raquel Fernández, to appear at EMNLP 2020.
• Refer, Reuse, Reduce: Grounding Subsequent References in Visual and Conversational Contexts by Ece Takmaz, Mario Giulianelli, Sandro Pezzelle, Arabella Sinclair and Raquel Fernández, to appear at EMNLP 2020.
• Be Different to Be Better! A Benchmark to Leverage the Complementarity of Language and Vision by Sandro Pezzelle, Claudio Greco, Greta Gandolfi, Eleonora Gualdoni, and Raffaella Bernardi, to appear at Findings of EMNLP 2020.

July 2020

• ACL2020: Mario will be presenting the paper Analysing Lexical Semantic Change with Contextualised Word Representations, Lieke the paper Learning to Understand Child-directed and Adult-directed Speech and Ece will be a live microblogger in the Language Grounding track (@ecekt2).
• Sandro will present an abstract on Asking questions with a big impact: Adapting to other interpretations of gradable adjectives at CogSci2020.

Spring 2020

• Sandro and Raquel were awarded a grant by the Research Priority Area Human(e) AI to carry out an interdisciplinary project on adaptation of conversational agents to different age groups.
• Ece has joined EACL's student board.

People

Resources

Datasets, code, and other publicly available resources developed by the Dialogue Modelling Group.

Language and vision

• MALeViC: Modeling Adjectives Leveraging Visual Contexts
  Dataset of synthetic images with pairs of statements and truth values related to size adjectives. See Pezzelle and Fernández 2019.
• The PhotoBook Dataset: Building Common Ground through Visually Grounded Dialogue
  A large-scale collection of 2,500 human-human, visually-grounded and goal-oriented English conversations between pairs of participants. Funded by ParlAI, Facebook AI Research. See Haber et al. 2019.
• VISTA: Visually Grounded Talking Agents
  Code and overview of research on visually-grounded language in interactive settings presented in a series of papers led jointly with Raffaella Bernardi.

Linguistic variation and change

• DUPS: Diachronic Usage Pair Similarity
  Dataset of similarity judgements of English word usage pairs from different time periods, as described by Giulianelli et al. 2020
• Short-term Meaning Shift
  Dataset of words annotated by users of the subredit r/LiverpoolFC for meaning change. See Del Tredici et al. 2019.
• The Road to Success
  Dataset from 20 Reddit forums (subedits) and code to reproduce the findings by Del Tredici and Fernández 2018.

Other topics

• The LAMBADA dataset: Word prediction requiring a broad discourse context
  Dataset to evaluate the capabilities of computational models for text understanding by means of a word prediction task. See Paperno et al. 2016.
• Corrective Feedback in First Language Acquisition
  Dataset of corrective feedback moves in CHILDES corpora annotated according to the taxonomy proposed by Hiller and Fernández 2016.
• How to Reject What in Dialogue
  Dataset of rejection moves from several dialogue corpora annotated according to the rejection taxonomy proposed by Schlöder and Fernández 2019.
• Collective Annotation
  Datasets and resources related to the problem of aggregating the judgements of multiple individuals, regarding a linguistic annotation task or similar, into a single collective judgement that reflects the view of the community. Project jointly led with Ulle Endriss.
• Quantified Referring Expressions
  Data from an online elicitation experiment on the use of quantified referring expressions.