Papers by Aditya Siddhant
SEAHORSE: A Multilingual, Multifaceted Dataset for Summarization Evaluation (2023.emnlp-main)
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Elizabeth Clark, Shruti Rijhwani, Sebastian Gehrmann, Joshua Maynez, Roee Aharoni, Vitaly Nikolaev, Thibault Sellam, Aditya Siddhant, Dipanjan Das, Ankur Parikh
| Challenge: | evaluating the quality of generated text is a difficult problem for large language models. |
| Approach: | They propose a dataset for multilingual, multifaceted summarization evaluation. |
| Outcome: | The proposed dataset can be used to train multilingual summarization systems . it shows that the dataset performs well on the out-of-domain meta-evaluation benchmarks TRUE and mFACE . |
Explicit Alignment Objectives for Multilingual Bidirectional Encoders (2021.naacl-main)
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| Challenge: | Pre-trained cross-lingual encoders have proven impressively effective at enabling transfer-learning of NLP systems from high-resource languages to low-resourced languages. |
| Approach: | They propose a method to align multilingual encoders using two explicit alignment objectives that align the multilingual representations at different granularities. |
| Outcome: | The proposed method achieves gains of up to 1.1 average F1 score on sequence tagging and 27.3 average accuracy on retrieval over the XLM-R-large model. |
DOCmT5: Document-Level Pretraining of Multilingual Language Models (2022.findings-naacl)
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| Challenge: | DOCmT5 is a multilingual sequence-to-sequence language model pretraining with large-scale parallel documents. |
| Approach: | They propose a multilingual sequence-to-sequence language model pretrained with large-scale parallel documents. |
| Outcome: | The proposed model improves on baselines on document-level generation tasks. |
Harnessing Multilinguality in Unsupervised Machine Translation for Rare Languages (2021.naacl-main)
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| Challenge: | Unsupervised translation systems have impressive performance on resource-rich language pairs . however, in more realistic settings, unsupervised systems perform poorly . |
| Approach: | They propose a model for 5 low-resource languages that leverages monolingual and auxiliary parallel data from other high-resourced languages. |
| Outcome: | The proposed model outperforms state-of-the-art models on low-resource languages . it also matches the current state- of-the art model for Nepali-English . |
nmT5 - Is parallel data still relevant for pre-training massively multilingual language models? (2021.acl-short)
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| Challenge: | Recent studies have shown that cross-lingual transfer learning in pre-trained multilingual models could be improved further by incorporating parallel data. |
| Approach: | They propose to integrate parallel data into mT5 pre-training to improve results on downstream multilingual and cross-lingual tasks. |
| Outcome: | The proposed model improves cross-lingual transfer significantly in small fine-tuning datasets and small model sizes. |
Deep Bayesian Active Learning for Natural Language Processing: Results of a Large-Scale Empirical Study (D18-1)
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| Challenge: | Existing studies on Active Learning (AL) for natural language processing have limited data requirements. |
| Approach: | They propose a Bayesian active learning approach that reduces deep learning's data dependence by comparing models and acquisition functions. |
| Outcome: | The proposed approach outperforms i.i.d. baselines and is more efficient than other approaches. |
XTREME-R: Towards More Challenging and Nuanced Multilingual Evaluation (2021.emnlp-main)
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Sebastian Ruder, Noah Constant, Jan Botha, Aditya Siddhant, Orhan Firat, Jinlan Fu, Pengfei Liu, Junjie Hu, Dan Garrette, Graham Neubig, Melvin Johnson
| Challenge: | Recent advances in multilingual natural language processing have improved performance on benchmarks such as XTREME and XGLUE by 13 points . however, improvements have been easier to achieve in some tasks than others . |
| Approach: | They extend XTREME to XTRAME-R, which includes ten natural language understanding tasks and covers 50 typologically diverse languages. |
| Outcome: | The proposed framework improves the performance on the XTREME multilingual benchmark by 13 points compared to human-level performance on English transfer learning. |
Leveraging Monolingual Data with Self-Supervision for Multilingual Neural Machine Translation (2020.acl-main)
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Aditya Siddhant, Ankur Bapna, Yuan Cao, Orhan Firat, Mia Chen, Sneha Kudugunta, Naveen Arivazhagan, Yonghui Wu
| Challenge: | Existing multilingual NMT approaches do not utilize the abundance of monolingual data, especially in low-resource languages. |
| Approach: | They propose to combine monolingual data with self-supervision to pre-train translation models and fine-tune on small amounts of supervised data. |
| Outcome: | The proposed approach improves translation quality of low-resource languages and zero-shot translation quality. |
Dialect-robust Evaluation of Generated Text (2023.acl-long)
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Jiao Sun, Thibault Sellam, Elizabeth Clark, Tu Vu, Timothy Dozat, Dan Garrette, Aditya Siddhant, Jacob Eisenstein, Sebastian Gehrmann
| Challenge: | Existing evaluation metrics that are not robust to dialect variation are difficult to measure for many groups of users and can penalize systems for producing text in lower-resource dialects. |
| Approach: | They propose a dialect-robust evaluation metric that produces the same score for system outputs that share the same semantics but are expressed in different dialects. |
| Outcome: | The proposed method significantly improves dialect robustness while preserving the correlation between automated metrics and human ratings. |
mT5: A Massively Multilingual Pre-trained Text-to-Text Transformer (2021.naacl-main)
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Linting Xue, Noah Constant, Adam Roberts, Mihir Kale, Rami Al-Rfou, Aditya Siddhant, Aditya Barua, Colin Raffel
| Challenge: | Current natural language processing pipelines often use transfer learning, where a model is pre-trained on a data-rich task before being fine-tuned on . this significantly limits their use given that roughly 80% of the world population does not speak English. |
| Approach: | They introduce a multilingual variant of T5 that was pre-trained on a new Common Crawl-based dataset covering 101 languages. |
| Outcome: | The proposed model achieves state-of-the-art on multilingual benchmarks and a simple technique to prevent accidental translation in the zero-shot setting. |