Papers by Bidisha Samanta
Entropy-guided Vocabulary Augmentation of Multilingual Language Models for Low-resource Tasks (2023.findings-acl)
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| Challenge: | Multilingual language models (MLLMs) support low-resource languages (LRLs) but LRL words are under-represented in wordpiece/subword vocabularies, leading to low task accuracy . |
| Approach: | They propose an entropy-based vocabulary augmented language model to detect LRL words with undesirable wordpiece segmentations. |
| Outcome: | The proposed model improves performance and limits on wordpiece augmentation strategies for multiple diverse LRLs. |
XTREME-UP: A User-Centric Scarce-Data Benchmark for Under-Represented Languages (2023.findings-emnlp)
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Sebastian Ruder, Jonathan Clark, Alexander Gutkin, Mihir Kale, Min Ma, Massimo Nicosia, Shruti Rijhwani, Parker Riley, Jean-Michel Sarr, Xinyi Wang, John Wieting, Nitish Gupta, Anna Katanova, Christo Kirov, Dana Dickinson, Brian Roark, Bidisha Samanta, Connie Tao, David Adelani, Vera Axelrod, Isaac Caswell, Colin Cherry, Dan Garrette, Reeve Ingle, Melvin Johnson, Dmitry Panteleev, Partha Talukdar
| Challenge: | Existing datasets are often informed by established research directions in the NLP community. |
| Approach: | They propose a benchmark to evaluate the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
| Outcome: | The proposed benchmark evaluates the capabilities of language models across 88 under-represented languages over 9 key user-centric technologies including ASR, OCR, MT, and information access tasks. |
Improved Sentiment Detection via Label Transfer from Monolingual to Synthetic Code-Switched Text (P19-1)
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| Challenge: | Existing sentiment detection methods are trained on sentiment-labeled monolingual text. |
| Approach: | They propose a method for synthesizing labeled code-switched text from monolingual text. |
| Outcome: | The proposed method improves sentiment labeling accuracy for three languages. |
Few-shot Controllable Style Transfer for Low-Resource Multilingual Settings (2022.acl-long)
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| Challenge: | Existing methods for few-shot style transfer often copy inputs verbatim . a new method is better at controlling the style transfer magnitude using an input scalar knob. |
| Approach: | They propose a method to model the stylistic difference between paraphrases by rewriting a sentence into a target style while preserving semantics. |
| Outcome: | The proposed method achieves 2-3x better performance in formality transfer and code-mixing addition across seven languages. |
Bootstrapping Multilingual Semantic Parsers using Large Language Models (2023.eacl-main)
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| Challenge: | Despite cross-lingual generalization, translation models require significant amounts of labeled data for many low-resource languages . brittle translation services may be due to domain mismatch between input text and general-purpose text . |
| Approach: | They propose to use large language models to translate English datasets into several languages via few-shot prompting. |
| Outcome: | The proposed method outperforms a strong translation-train baseline on 41 out of 50 languages. |
A Hierarchical VAE for Calibrating Attributes while Generating Text using Normalizing Flow (2021.acl-long)
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| Challenge: | Existing text-style transfer techniques focus on flipping the style attribute polarity instead of fine-grained regulation of attributes to generate multiple variants of a sentence. |
| Approach: | They propose a hierarchical architecture for finer control over the attribute, preserving content using attribute dis- entanglement. |
| Outcome: | The proposed framework generates natural looking sentences with finer control of intensity of a given attribute. |