Papers by Abhirut Gupta
Improving Segmentation for Technical Support Problems (2020.acl-main)
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| Challenge: | Technical support problems are long and complex and cannot be correctly parsed by tools designed for natural language. |
| Approach: | They propose a sequence labelling task and a supervised text segmentation approach to solve this problem. |
| Outcome: | The proposed approach improves on the downstream task of answer retrieval. |
Semantic Parsing for Technical Support Questions (C18-1)
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| Challenge: | Automating technical support is a task of providing answers to complex problems . traditional approaches to this task rely on information retrieval and are keyword based . |
| Approach: | They propose a semantic parsing approach that uses grammatical structure to extract technical questions' attributes as a baseline and a CRF-based model that can improve performance in the presence of annotated data. |
| Outcome: | The proposed model outperforms retrieval baselines in annotated data for training. |
Training Data Augmentation for Code-Mixed Translation (2021.naacl-main)
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| Challenge: | We show a 5.8 point increase in BLEU on heavily code-mixed sentences . code-mixing is becoming more commonplace in several bilingual communities . |
| Approach: | They propose a method to convert existing parallel data sources into code-mixed parallel data. |
| Outcome: | The proposed method shows a 5.8 point increase in BLEU on heavily code-mixed sentences on a Hindi-English code-mixed translation task. |
Adapting Multilingual Models for Code-Mixed Translation (2022.findings-emnlp)
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| Challenge: | Prior work has addressed the lack of gold standard code-mixed to pure language parallel data with data augmentation techniques. |
| Approach: | They propose a back-translation-based training scheme for code-mixed translation which eliminates dependence on external resources. |
| Outcome: | The proposed model beats previous work by up to +3.8 BLEU on code-mixed tasks. |
Bi-Phone: Modeling Inter Language Phonetic Influences in Text (2023.acl-long)
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Abhirut Gupta, Ananya B. Sai, Richard Sproat, Yuri Vasilevski, James Ren, Ambarish Jash, Sukhdeep Sodhi, Aravindan Raghuveer
| Challenge: | Increasingly, people are forced to use the Web in languages they have low literacy in due to technology asymmetries. |
| Approach: | They propose a method to mine phoneme confusions for pairs of L1 and L2 and plug them into a generative model for synthetically producing corrupted L2 text. |
| Outcome: | The proposed method corrupts the popular language understanding benchmark SuperGLUE and improves performance. |
HintedBT: Augmenting Back-Translation with Quality and Transliteration Hints (2021.emnlp-main)
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| Challenge: | HintedBT provides hints (as source tags on the encoder) about the quality of each source-target pair. |
| Approach: | They propose a method which provides hints to the encoder and decoder to improve the quality of BT data by providing hints about the quality. |
| Outcome: | The proposed method improves translation quality and performance in three low/medium-resource language pairs. |