Thamme Gowda


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Findings of the 2023 Conference on Machine Translation (WMT23): LLMs Are Here but Not Quite There Yet
Tom Kocmi | Eleftherios Avramidis | Rachel Bawden | Ondřej Bojar | Anton Dvorkovich | Christian Federmann | Mark Fishel | Markus Freitag | Thamme Gowda | Roman Grundkiewicz | Barry Haddow | Philipp Koehn | Benjamin Marie | Christof Monz | Makoto Morishita | Kenton Murray | Makoto Nagata | Toshiaki Nakazawa | Martin Popel | Maja Popović | Mariya Shmatova
Proceedings of the Eighth Conference on Machine Translation

This paper presents the results of the General Machine Translation Task organised as part of the 2023 Conference on Machine Translation (WMT). In the general MT task, participants were asked to build machine translation systems for any of 8 language pairs (corresponding to 14 translation directions), to be evaluated on test sets consisting of up to four different domains. We evaluate system outputs with professional human annotators using a combination of source-based Direct Assessment and scalar quality metric (DA+SQM).

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Findings of the WMT 2023 Shared Task on Parallel Data Curation
Steve Sloto | Brian Thompson | Huda Khayrallah | Tobias Domhan | Thamme Gowda | Philipp Koehn
Proceedings of the Eighth Conference on Machine Translation

Building upon prior WMT shared tasks in document alignment and sentence filtering, we posed the open-ended shared task of finding the best subset of possible training data from a collection of Estonian-Lithuanian web data. Participants could focus on any portion of the end-to-end data curation pipeline, including alignment and filtering. We evaluated results based on downstream machine translation quality. We release processed Common Crawl data, along with various intermediate states from a strong baseline system, which we believe will enable future research on this topic.

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Cometoid: Distilling Strong Reference-based Machine Translation Metrics into Even Stronger Quality Estimation Metrics
Thamme Gowda | Tom Kocmi | Marcin Junczys-Dowmunt
Proceedings of the Eighth Conference on Machine Translation

This paper describes our submissions to the 2023 Conference on Machine Translation (WMT-23) Metrics shared task. Knowledge distillation is commonly used to create smaller student models that mimic larger teacher model while reducing the model size and hence inference cost in production. In this work, we apply knowledge distillation to machine translation evaluation metrics and distill existing reference-based teacher metrics into reference-free (quality estimation; QE) student metrics. We mainly focus on students of Unbabel’s COMET22 reference-based metric. When evaluating on the official WMT-22 Metrics evaluation task, our distilled Cometoid QE metrics outperform all other QE metrics on that set while matching or out-performing the reference-based teacher metric. Our metrics never see the human ground-truth scores directly – only the teacher metric was trained on human scores by its original creators. We also distill ChrF sentence-level scores into a neural QE metric and find that our reference-free (and fully human-score-free) student metric ChrFoid outperforms its teacher metric by over 7% pairwise accuracy on the same WMT-22 task, rivaling other existing QE metrics.

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SOTASTREAM: A Streaming Approach to Machine Translation Training
Matt Post | Thamme Gowda | Roman Grundkiewicz | Huda Khayrallah | Rohit Jain | Marcin Junczys-Dowmunt
Proceedings of the 3rd Workshop for Natural Language Processing Open Source Software (NLP-OSS 2023)

Many machine translation toolkits make use of a data preparation step wherein raw data is transformed into a tensor format that can be used directly by the trainer. This preparation step is increasingly at odds with modern research and development practices because this process produces a static, unchangeable version of the training data, making common training-time needs difficult (e.g., subword sampling), time-consuming (preprocessing with large data can take days), expensive (e.g., disk space), and cumbersome (managing experiment combinatorics). We propose an alternative approach that separates the generation of data from the consumption of that data. In this approach, there is no separate pre-processing step; data generation produces an infinite stream of permutations of the raw training data, which the trainer tensorizes and batches as it is consumed. Additionally, this data stream can be manipulated by a set of user-definable operators that provide on-the-fly modifications, such as data normalization, augmentation or filtering. We release an open-source toolkit, SOTASTREAM, that implements this approach: We show that it cuts training time, adds flexibility, reduces experiment management complexity, and reduces disk space, all without affecting the accuracy of the trained models.


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Findings of the 2022 Conference on Machine Translation (WMT22)
Tom Kocmi | Rachel Bawden | Ondřej Bojar | Anton Dvorkovich | Christian Federmann | Mark Fishel | Thamme Gowda | Yvette Graham | Roman Grundkiewicz | Barry Haddow | Rebecca Knowles | Philipp Koehn | Christof Monz | Makoto Morishita | Masaaki Nagata | Toshiaki Nakazawa | Michal Novák | Martin Popel | Maja Popović
Proceedings of the Seventh Conference on Machine Translation (WMT)

This paper presents the results of the General Machine Translation Task organised as part of the Conference on Machine Translation (WMT) 2022. In the general MT task, participants were asked to build machine translation systems for any of 11 language pairs, to be evaluated on test sets consisting of four different domains. We evaluate system outputs with human annotators using two different techniques: reference-based direct assessment and (DA) and a combination of DA and scalar quality metric (DA+SQM).


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Many-to-English Machine Translation Tools, Data, and Pretrained Models
Thamme Gowda | Zhao Zhang | Chris Mattmann | Jonathan May
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing: System Demonstrations

While there are more than 7000 languages in the world, most translation research efforts have targeted a few high resource languages. Commercial translation systems support only one hundred languages or fewer, and do not make these models available for transfer to low resource languages. In this work, we present useful tools for machine translation research: MTData, NLCodec and RTG. We demonstrate their usefulness by creating a multilingual neural machine translation model capable of translating from 500 source languages to English. We make this multilingual model readily downloadable and usable as a service, or as a parent model for transfer-learning to even lower-resource languages.

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Macro-Average: Rare Types Are Important Too
Thamme Gowda | Weiqiu You | Constantine Lignos | Jonathan May
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

While traditional corpus-level evaluation metrics for machine translation (MT) correlate well with fluency, they struggle to reflect adequacy. Model-based MT metrics trained on segment-level human judgments have emerged as an attractive replacement due to strong correlation results. These models, however, require potentially expensive re-training for new domains and languages. Furthermore, their decisions are inherently non-transparent and appear to reflect unwelcome biases. We explore the simple type-based classifier metric, MacroF1, and study its applicability to MT evaluation. We find that MacroF1 is competitive on direct assessment, and outperforms others in indicating downstream cross-lingual information retrieval task performance. Further, we show that MacroF1 can be used to effectively compare supervised and unsupervised neural machine translation, and reveal significant qualitative differences in the methods’ outputs.


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Finding the Optimal Vocabulary Size for Neural Machine Translation
Thamme Gowda | Jonathan May
Findings of the Association for Computational Linguistics: EMNLP 2020

We cast neural machine translation (NMT) as a classification task in an autoregressive setting and analyze the limitations of both classification and autoregression components. Classifiers are known to perform better with balanced class distributions during training. Since the Zipfian nature of languages causes imbalanced classes, we explore its effect on NMT. We analyze the effect of various vocabulary sizes on NMT performance on multiple languages with many data sizes, and reveal an explanation for why certain vocabulary sizes are better than others.


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Cross-lingual Joint Entity and Word Embedding to Improve Entity Linking and Parallel Sentence Mining
Xiaoman Pan | Thamme Gowda | Heng Ji | Jonathan May | Scott Miller
Proceedings of the 2nd Workshop on Deep Learning Approaches for Low-Resource NLP (DeepLo 2019)

Entities, which refer to distinct objects in the real world, can be viewed as language universals and used as effective signals to generate less ambiguous semantic representations and align multiple languages. We propose a novel method, CLEW, to generate cross-lingual data that is a mix of entities and contextual words based on Wikipedia. We replace each anchor link in the source language with its corresponding entity title in the target language if it exists, or in the source language otherwise. A cross-lingual joint entity and word embedding learned from this kind of data not only can disambiguate linkable entities but can also effectively represent unlinkable entities. Because this multilingual common space directly relates the semantics of contextual words in the source language to that of entities in the target language, we leverage it for unsupervised cross-lingual entity linking. Experimental results show that CLEW significantly advances the state-of-the-art: up to 3.1% absolute F-score gain for unsupervised cross-lingual entity linking. Moreover, it provides reliable alignment on both the word/entity level and the sentence level, and thus we use it to mine parallel sentences for all (302, 2) language pairs in Wikipedia.

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SARAL: A Low-Resource Cross-Lingual Domain-Focused Information Retrieval System for Effective Rapid Document Triage
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
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics: System Demonstrations

With the increasing democratization of electronic media, vast information resources are available in less-frequently-taught languages such as Swahili or Somali. That information, which may be crucially important and not available elsewhere, can be difficult for monolingual English speakers to effectively access. In this paper we present an end-to-end cross-lingual information retrieval (CLIR) and summarization system for low-resource languages that 1) enables English speakers to search foreign language repositories of text and audio using English queries, 2) summarizes the retrieved documents in English with respect to a particular information need, and 3) provides complete transcriptions and translations as needed. The SARAL system achieved the top end-to-end performance in the most recent IARPA MATERIAL CLIR+summarization evaluations. Our demonstration system provides end-to-end open query retrieval and summarization capability, and presents the original source text or audio, speech transcription, and machine translation, for two low resource languages.