Papers by Irshad Bhat
wikiHowToImprove: A Resource and Analyses on Edits in Instructional Texts (2020.lrec-1)
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| Challenge: | wikiHow articles are subject to revision edits, but do they provide clarifications? a new study compares changes made across multiple versions of the same set of instructions . |
| Approach: | They use wikiHow to analyze revision histories for 2.7 million sentences from wikihow . they use human annotation to categorize subset of edits and provide models . |
| Outcome: | The proposed model can distinguish between “older” and “newer” revisions of a sentence. |
Answering Naturally: Factoid to Full length Answer Generation (D19-54)
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| Challenge: | Factoid question answering systems extract answers for a question from passages, which are usually short spans of text . but, these spans would result in an unnatural reading experience in a conversational system . a pointer generator based full-length answer generator can be used with most QA systems . |
| Approach: | They propose a pointer generator based full-length answer generator which can be used with most QA systems. |
| Outcome: | The proposed system generates full length answer without relying on passage from which it was extracted. |
Towards Modeling Revision Requirements in wikiHow Instructions (2020.emnlp-main)
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| Challenge: | wikiHow is a collaboratively edited platform of how-to guides . authors extend existing textual edits with 4 million sentences that remain unedited . |
| Approach: | They extend existing textual edits with a set of 4 million sentences that remain unedited over time. |
| Outcome: | The proposed model can predict the need for edits in wikiHow guides . the authors extend an existing resource of textual edits with a complementary set of 4 million sentences that remain unedited over time . |
Universal Dependency Parsing for Hindi-English Code-Switching (N18-1)
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| Challenge: | Code-switching data often need additional processes such as language identification, normalization and/or back-transliteration to be processed. |
| Approach: | They propose a neural stacking model that leverages part-of-speech tags and syntactic tree annotations in tweets to parse code-switching data. |
| Outcome: | The proposed model is 1.5% better than the augmented model and 3.8% better than one which uses first-best normalization and/or back-transliteration. |