Papers by Emmanuel Vincent
Chop and Change: Anaphora Resolution in Instructional Cooking Videos (2022.findings-aacl)
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| Challenge: | temporally evolving entities present challenges for anaphora resolution tasks . recipes provide rich source for referring expressions of transformed entities . |
| Approach: | They propose to use annotations to annotate recipes for anaphora resolution task . they propose to employ temporal features to improve anamorphic resolution . |
| Outcome: | The proposed annotation scheme improves the performance of the anaphora resolution task. |
MMAR: Multilingual and Multimodal Anaphora Resolution in Instructional Videos (2024.findings-emnlp)
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| Challenge: | Existing approaches to multilingual anaphora resolution include images and video inputs. |
| Approach: | They propose to include multimodal information in the form of images in anaphora resolution tasks. |
| Outcome: | The proposed approach improves resolution by 10% for unseen languages. |
QUARTZ: QA-based Unsupervised Abstractive Refinement for Task-oriented Dialogue Summarization (2025.findings-emnlp)
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| Challenge: | a framework for task-oriented utility-based dialogue summarization is proposed . QUARTZ is a tool for task summarizing dialogues, but its outputs lack task-specific focus. |
| Approach: | They propose a framework for task-oriented utility-based dialogue summarization . QUARTZ generates summaries and question-answer pairs from a dialogue in a zero-shot manner . |
| Outcome: | The proposed framework achieves competitive results in zero-shot settings, rivaling fully-supervised State-of-the-Art methods. |
Find-2-Find: Multitask Learning for Anaphora Resolution and Object Localization (2023.emnlp-main)
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| Challenge: | Existing systems require large number of accurate annotations, such as image-level labels and location-level labeling. |
| Approach: | They propose a joint anaphora resolution and object localization dataset targeting visual-linguistic ambiguity. |
| Outcome: | The proposed framework improves visual-linguistic alignment and object localization with one joint model compared to a strong single-task baseline. |
Adapting Language Models When Training on Privacy-Transformed Data (2022.lrec-1)
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| Challenge: | Using data sanitization methods to remove personal information from spoken messages is not effective because privacy-transformed data is unlikely to match the test distribution. |
| Approach: | They propose to use a data sanitization approach to remove personal information from spoken messages by replacing named entities with other words from the same class. |
| Outcome: | The proposed approach removes personal information from the spoken messages using an automatic named entity recognition method. |
Transformer versus LSTM Language Models trained on Uncertain ASR Hypotheses in Limited Data Scenarios (2022.lrec-1)
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| Challenge: | Existing studies show that domain-specific LMs can only rely on limited in-domain speech data . a qualitative analysis reveals that Transformer LM can predict less frequent words . |
| Approach: | They propose a method to train Transformer LMs on ASR confusion networks . they find they are better at exploiting alternate uncertain ASR hypotheses . |
| Outcome: | The proposed method reduces perplexity by 3-6% on AMI scenarios but performs similar to LSTM LMs on Verbmobil conversational corpus. |