Papers by Asad Sayeed
An Annotation Approach for Social and Referential Gaze in Dialogue (2020.lrec-1)
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| Challenge: | Existing studies on eye gaze information focus on social functions and how it is used in reference resolution. |
| Approach: | They propose an approach for annotating eye gaze considering its social and referential functions in multi-modal dialogue. |
| Outcome: | The proposed annotation scheme is based on eye gaze behavior cues in human-human dialogues. |
Exploiting Cross-Lingual Hints to Discover Event Pronouns (2020.lrec-1)
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| Challenge: | Non-nominal co-reference is much less studied than nominal coreference because of the lack of annotated corpora. |
| Approach: | They propose to use parallel multilingual corpora to create artificially labeled data for the classification of three different readings of the English pronoun ‘it’: entity, event or pleonastic. |
| Outcome: | The proposed method can be used to classify three different readings of the English pronoun ‘it’ from their translation in several languages. |
Rollenwechsel-English: a large-scale semantic role corpus (L18-1)
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| Challenge: | The Rollenwechsel-English corpus is a large corpus of automatically-labelled semantic frames extracted from the ukWaC corpus and BNC using Propbank roles. |
| Approach: | They present a large corpus of automatically-labelled semantic frames extracted from ukWaC and BNC using Propbank roles. |
| Outcome: | The rollenwechsel-English corpus is a large corpus of automatically-labelled semantic frames extracted from the ukWaC corpus and BNC using Propbank roles. |
Visual Coherence Loss for Coherent and Visually Grounded Story Generation (2023.findings-acl)
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| Challenge: | Existing visual storytelling models fail to generate correct referring expressions for characters, causing 60% of the generated stories to be lacking local coherence. |
| Approach: | They propose a loss function inspired by a linguistic theory of coherence for self-supervised learning for image sequence representations and a feature matching metric to check whether the models generate referring expressions correctly for characters in input image sequences. |
| Outcome: | The proposed features and loss function are effective for generating more coherent and visually grounded stories. |
Thematic Fit Bits: Annotation Quality and Quantity Interplay for Event Participant Representation (2022.lrec-1)
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| Challenge: | linguistically machine-annotated large corpus requires a large burden of labeled data. |
| Approach: | They compare linguistically machine-annotated large corpus output with higher-quality taggers to model thematic fit using a high-performing neural approach. |
| Outcome: | The proposed model shows that quality improves with training size, but plateaus or declines with size. |
Visual Writing Prompts: Character-Grounded Story Generation with Curated Image Sequences (2023.tacl-1)
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| Challenge: | Existing work on image-based story generation lacks coherent plots for story generation. |
| Approach: | They propose to use image sequences to generate stories from a dataset that has more coherent plots. |
| Outcome: | The proposed model produces more coherent, visually grounded and diverse stories than existing models. |
Semantic shift in social networks (2021.starsem-1)
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| Challenge: | lexical semantic change manifests differently across different communities, according to a new study . social network analysis is a tool of sociolinguists studying variation and change . |
| Approach: | They use distributional methods to quantify lexical semantic change and induce a social network on communities based on interactions between members. |
| Outcome: | The proposed method is based on interactions between members and the community. |