Papers by Le An Ha
Verbal Multiword Expressions for Identification of Metaphor (2020.acl-main)
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| Challenge: | Metaphor is a linguistic device in which a concept is expressed by mentioning another . Verbal MWEs are examples of non-literal language in which multiple words form a single unit of meaning. |
| Approach: | They propose to analyze the interplay between metaphor and multiword expressions processing by informing the model of the presence of MWEs. |
| Outcome: | The proposed architecture reach state-of-the-art on two established metaphor datasets. |
Error Analysis of NLP Models and Non-Native Speakers of English Identifying Sarcasm in Reddit Comments (2024.lrec-main)
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| Challenge: | sarcasm detection remains an issue for both humans and natural language processing models . |
| Approach: | They analysed 300 comments from the FigLang 2020 Reddit Dataset and 39 non-native speakers of English to see if they were sarcastic. |
| Outcome: | The results show that the models and models have similar performance and weaknesses when the comments include political topics or are phrased as questions. |
Bridging the Gap: Attending to Discontinuity in Identification of Multiword Expressions (N19-1)
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| Challenge: | Existing approaches to identify discontinuous multiword expressions are limited in dealing with discontinuous occurrences. |
| Approach: | They propose a method to tag Multiword Expressions using a language-independent deep learning architecture to target discontinuity. |
| Outcome: | The proposed model outperforms baseline models on a multilingual dataset and scores higher than baseline models. |
Automated Prediction of Examinee Proficiency from Short-Answer Questions (2020.coling-main)
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| Challenge: | Existing approaches to predicting examinee proficiency from short-answer questions (SAQs) use of labeled data to train on is difficult, and requires expensive expert-rated data. |
| Approach: | They propose a method to predict examinee proficiency from short-answer questions . previous approaches train on manually labeled data to predict human-ratings assigned to SAQs . |
| Outcome: | The proposed model examines examinee proficiency directly and does not require manual training on labeled data. |
Predicting Item Survival for Multiple Choice Questions in a High-Stakes Medical Exam (2020.lrec-1)
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| Challenge: | Existing methods of pretesting are costly and require a large pool of test questions to be replenished, updated and expanded over time. |
| Approach: | They propose to automatically predict an item's probability to "survive" pretesting by embedding new items within a live exam and analyzing the responses. |
| Outcome: | The proposed method is based on human-produced MCQs for a medical exam and shows that survival is modelled through linguistic features and embedding types and features inspired by information retrieval. |
A First Dataset for Film Age Appropriateness Investigation (2020.lrec-1)
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| Challenge: | Several studies in CL have examined the genre in terms of Sentiment Analysis (Phan and Matsumoto, 2018) . but the actual film content (the script, audio, and video for example) has not received as much interest in spite of the potential availability of huge datasets. |
| Approach: | They use a corpus of 17000 films along with their age ratings to predict the correct age classification for the United States and the UK. |
| Outcome: | The proposed model outperforms FastText and Deep Learning architectures in the US and UK and is 79.3% accurate for the US compared to 80% for the UK. |
Classifying Referential and Non-referential It Using Gaze (D18-1)
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| Challenge: | a particular problem for anaphora resolution systems is the pronoun it, which can be used both referentially and non-referentially. |
| Approach: | They use eye-tracking data to learn how humans perform disambiguation and use it to improve automatic classification. |
| Outcome: | The proposed system outperforms a baseline and outperformed linguistic-based approaches. |