Raquel Fernández

Resources

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

• 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.
• 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.
• 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.
• 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.
• 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.
• 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.