Papers by Thamme Gowda
Macro-Average: Rare Types Are Important Too (2021.naacl-main)
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| Challenge: | MT metrics trained on segment-level human judgments are inherently non-transparent and reflect undesirable biases. |
| Approach: | They propose to use a type-based classifier metric to evaluate machine translation and compare it with a supervised and unsupervised one. |
| Outcome: | The proposed model outperforms other models in indicating cross-lingual information retrieval task performance and shows that it can be used to compare supervised and unsupervised neural machine translation. |
Many-to-English Machine Translation Tools, Data, and Pretrained Models (2021.acl-demo)
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| Challenge: | Commercial translation systems support only one hundred languages or fewer . commercial translation systems do not make these models available for transfer to low resource languages . |
| Approach: | They propose a multilingual neural machine translation model that can translate from 500 source languages to English. |
| Outcome: | The proposed model can translate from 500 source languages to English, or be used as a parent model for low-resource languages. |
Finding the Optimal Vocabulary Size for Neural Machine Translation (2020.findings-emnlp)
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| Challenge: | Class imbalance is said to exist when one or more classes are not of approximately equal frequency in data. |
| Approach: | They cast neural machine translation (NMT) as a classification task in an autoregressive setting and examine its limitations. |
| Outcome: | The proposed model performs better on multiple languages with large data sizes with different vocabulary sizes. |
BotEval: Facilitating Interactive Human Evaluation (2024.acl-demos)
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| Challenge: | Using language models to perform complex interactive tasks is becoming more common with the rapid progress in natural language processing (NLP) models. |
| Approach: | They develop an evaluation toolkit that enables human-bot interactions as part of the evaluation process. |
| Outcome: | The evaluation toolkit enables human-bot interactions as part of the evaluation process, rather than making judgements for a static input. |
Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining (D19-61)
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| Challenge: | Entities can be used as effective signals to generate less ambiguous semantic representations and align multiple languages. |
| Approach: | They propose a method to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. |
| Outcome: | The proposed method can generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia . it provides reliable alignment on word/entity level and sentence level, and thus can be used for unsupervised cross-linguistic entity linking. |
PyMarian: Fast Neural Machine Translation and Evaluation in Python (2024.emnlp-demo)
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| Challenge: | a Python interface to Marian NMT is available in PyPI via pip install pymarian . the interface provides a speedup factor of up to 7.8 the existing implementations . |
| Approach: | They propose a Python interface to Marian NMT, a C++-based training and inference toolkit for sequence-to-sequence models. |
| Outcome: | The proposed interface enables models trained with Marian to be connected to Python tools with a speedup factor of up to 7.8 the existing implementations. |
SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage (P19-3)
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Elizabeth Boschee, Joel Barry, Jayadev Billa, Marjorie Freedman, Thamme Gowda, Constantine Lignos, Chester Palen-Michel, Michael Pust, Banriskhem Kayang Khonglah, Srikanth Madikeri, Jonathan May, Scott Miller
| Challenge: | a new cross-lingual information retrieval system for low-resource languages is available in less-frequently-taught languages . a multilingual system can search for relevant information in a haystack of documents in swahili or Somali . human-driven approaches to this problem are complicated in 'low-resourced' languages aaron sagar: "the key role played by humans in triaging results is complicated" |
| Approach: | They propose an end-to-end cross-lingual information retrieval system for low-resource languages . the system enables English speakers to search foreign language repositories using English queries . it summarizes the retrieved documents in English with respect to a particular information need . |
| Outcome: | The proposed system achieves top performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. |