Papers by Raquel Fernandez
Ask No More: Deciding when to guess in referential visual dialogue (C18-1)
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| Challenge: | Using a task-oriented visual dialogue model, we add 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. |
| Approach: | They augment 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. |
| Outcome: | The proposed model can be enhanced 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. |
Speaking the Language of Your Listener: Audience-Aware Adaptation via Plug-and-Play Theory of Mind (2023.findings-acl)
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| Challenge: | Adaptation is a process in human communication by which a speaker tunes its language to that of a listener to achieve communicative success. |
| Approach: | They propose a visual-based referential game between a knowledgeable speaker and a listener with limited visual and linguistic experience to model this adaptation mechanism. |
| Outcome: | The proposed model improves on plug-and-play approaches to controlled language generation without finetuning the speaker’s underlying language model. |
Stop Measuring Calibration When Humans Disagree (2022.emnlp-main)
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| Challenge: | Calibration is a popular framework to evaluate whether a classifier knows when it does not know - i.e., predictive probabilities are a good indication of how likely a prediction is to be correct. |
| Approach: | They propose to measure calibration to human majority given inherent disagreements on tasks where humans inherently disagree about which class applies. |
| Outcome: | The proposed measures capture key statistical properties of human judgements including class frequency, ranking and entropy. |
Interpretable Word Sense Representations via Definition Generation: The Case of Semantic Change Analysis (2023.acl-long)
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| Challenge: | Existing approaches to semantic change analysis are limited in their interpretation power and lack of explanatory power. |
| Approach: | They propose to use specialised Flan-T5 language models to generate a definition for each usage and a specialised word sense model to generate the most prototypical definition. |
| Outcome: | The proposed representations outperform token or usage sentence embeddings in word-in-context semantic similarity judgements and are a promising type of lexical representation for NLP. |