Papers by Elsbeth Turcan
Segmenting Subtitles for Correcting ASR Segmentation Errors (2021.eacl-main)
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David Wan, Chris Kedzie, Faisal Ladhak, Elsbeth Turcan, Petra Galuscakova, Elena Zotkina, Zhengping Jiang, Peter Bell, Kathleen McKeown
| Challenge: | Typical ASR systems segment input audio into utterances using purely acoustic information, which may not resemble sentence-like units expected by conventional machine translation systems for spoken language translation (SLT). |
| Approach: | They propose a model for correcting ASR acoustic segmentation using subtitles as a proxy dataset for creating synthetic aural utterances by modeling common error modes. |
| Outcome: | The proposed model improves performance on MT and audio-document cross-language information retrieval (CLIR) it uses subtitles as a proxy dataset to correct ASR acoustic segmentation . |
MASIVE: Open-Ended Affective State Identification in English and Spanish (2024.emnlp-main)
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| Challenge: | Existing models that fail to understand cultural and language influences the meaning of emotional terms like "love" a new study shows that smaller finetuned models outperform much larger LLMs on region-specific span prediction tasks. |
| Approach: | They propose to use a reddit reddits dataset to identify a set of affective states . they find that smaller finetuned multilingual models outperform larger LLMs . |
| Outcome: | The proposed model outperforms larger models on span prediction task even on region-specific Spanish affective states. |
Emotion-Infused Models for Explainable Psychological Stress Detection (2021.naacl-main)
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| Challenge: | a new study examines the use of emotion detection for detecting psychological stress in online posts . traditional multi-task learning and emotion-based language model fine-tuning are used to improve the model . |
| Approach: | They propose to use a semantically related task, emotion detection, for detecting psychological stress in online posts . they propose multi-task learning and emotion-based language model fine-tuning to improve the model . |
| Outcome: | The proposed model is more explainable and human-like than a black-box model . the proposed model mirrors psychological components of stress, the authors show . |
Dreaddit: A Reddit Dataset for Stress Analysis in Social Media (D19-62)
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| Challenge: | Existing computational studies on stress only focus on domains such as speech or Twitter . a corpus of social media text is used to identify stress . |
| Approach: | They propose a text corpus of lengthy social media data for detecting stress . they use 190K posts from five different categories of Reddit communities . |
| Outcome: | The proposed corpus of social media data can be used to identify stress . it includes 190K posts from five different categories of Reddit communities . |
Constrained Regeneration for Cross-Lingual Query-Focused Extractive Summarization (2022.coling-1)
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Elsbeth Turcan, David Wan, Faisal Ladhak, Petra Galuscakova, Sukanta Sen, Svetlana Tchistiakova, Weijia Xu, Marine Carpuat, Kenneth Heafield, Douglas Oard, Kathleen McKeown
| Challenge: | Query-focused summarization of foreign-language documents can help a user understand whether a document is relevant to a query term. |
| Approach: | They propose to use machine translation and post-editing to improve human relevance judgments . they include a query term in a summary when its translation appears in the source document . |
| Outcome: | The proposed approach improves human relevance judgments by including a query term in a summary when its translation appears in the source document. |
Multi-Task Learning and Adapted Knowledge Models for Emotion-Cause Extraction (2021.findings-acl)
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Elsbeth Turcan, Shuai Wang, Rishita Anubhai, Kasturi Bhattacharjee, Yaser Al-Onaizan, Smaranda Muresan
| Challenge: | Detecting what emotions are expressed in text is a well-studied problem in natural language processing. |
| Approach: | They propose methods that combine common-sense knowledge with multi-task learning to perform joint emotion classification and emotion cause tagging. |
| Outcome: | The proposed models improve on both tasks when using common-sense reasoning and a multitask framework. |
Evaluation of African American Language Bias in Natural Language Generation (2023.emnlp-main)
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| Challenge: | Existing studies have shown that large language generation models disadvantaging African American Language (AAL) can be biased for certain language varieties, but there is little research on the impact of these biases on other languages. |
| Approach: | They evaluate how well LLMs understand African American Language (AAL) in comparison to white Mainstream English (WME) using a dataset of AAL texts from a variety of regions and contexts, they find dialectal bias in six pre-trained LLM. |
| Outcome: | The proposed models understand African American language in comparison to white mainstream English (WME) the proposed models have performance gaps on two tasks that are not matched by the model. |