Papers by Ankur Parikh
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 . |
Real-Time Open-Domain Question Answering with Dense-Sparse Phrase Index (P19-1)
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| Challenge: | Existing open-domain question answering models require multiple documents on-demand for every input query. |
| Approach: | They propose query-agnostic indexable representations of document phrases that can drastically speed up open-domain question answering. |
| Outcome: | The proposed model can be trained and deployed even in a single 4-GPU server. |
Handling Divergent Reference Texts when Evaluating Table-to-Text Generation (P19-1)
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| Challenge: | Existing text generation metrics rely on reference texts, such as BLEU and ROUGE, but they are too expensive to apply repeatedly. |
| Approach: | They propose a metric which aligns n-grams from the generated texts to the semi-structured data before computing their precision and recall. |
| Outcome: | The proposed metric correlates with human judgments better than existing text generation metrics while being easier to use. |
Phrase-Indexed Question Answering: A New Challenge for Scalable Document Comprehension (D18-1)
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| Challenge: | Existing QA models rely on learning interaction between document and question . current models require explicit attention to the document before or as it reads it . |
| Approach: | They propose a modular question answering task that enforces complete independence of the document encoder from the question encoder. |
| Outcome: | The proposed model achieves reasonable accuracy but significantly underperforms unconstrained QA models. |
Learning with Limited Text Data (2022.acl-tutorials)
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| Challenge: | Natural Language Processing (NLP) relies on labeled data to perform state-of-the-art performance . labeles are often required to label large amounts of textual data . this tutorial will provide an overview of labeleing in NLP . |
| Approach: | This tutorial will provide a systematic overview of methods for learning from limited labeled data. |
| Outcome: | This tutorial will provide a systematic and up-to-date overview of the proposed methods . it will highlight current challenges and future directions . |
A Multilingual View of Unsupervised Machine Translation (2020.findings-emnlp)
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| Challenge: | Empirically, we show that our approach results in higher BLEU scores over state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
| Approach: | They propose a probabilistic framework for multilingual neural machine translation that encompasses supervised and unsupervised setups, focusing on unsupervised translation. |
| Outcome: | The proposed framework achieves higher BLEU scores than state-of-the-art unsupervised models on the WMT’14 English-French, WMT'16 English-German, and WMT‘16 English–Romanian datasets in most directions. |
Text Generation with Exemplar-based Adaptive Decoding (N19-1)
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| Challenge: | Empirical results show that the proposed model achieves strong performance and outperforms comparable baselines. |
| Approach: | They propose a conditioned text generation model that uses a template-based approach to generate content from input text. |
| Outcome: | The proposed model outperforms baselines on abstractive text summarization and data-to-text generation. |
Learning Compact Metrics for MT (2021.emnlp-main)
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| Challenge: | Recent advances in machine translation and multilingual text generation have led researchers to adopt trained metrics such as COMET or BLEURT, which treat evaluation as a regression problem and use representations from multilingual pre-trained models such as XLM-RoBERTa or mBERT. |
| Approach: | They propose to use multilingual model capacity to improve model performance by transferring knowledge from one teacher to multiple students trained on related languages. |
| Outcome: | The proposed model yields 10.5% improvement over vanilla fine-tuning and reaches 92.6% of RemBERT’s performance using only a third of its parameters. |
Consistency by Agreement in Zero-Shot Neural Machine Translation (N19-1)
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| Challenge: | In this paper, we focus on zero-shot generalization—a challenging setup that tests models on translation directions they have not been optimized for at training time. |
| Approach: | They propose a method that allows for a consistent agreement-based training method that encourages the model to produce equivalent translations of parallel sentences in auxiliary languages. |
| Outcome: | The proposed model improves on public zero-shot translation benchmarks without loss of performance on supervised translation directions. |
ToTTo: A Controlled Table-To-Text Generation Dataset (2020.emnlp-main)
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Ankur Parikh, Xuezhi Wang, Sebastian Gehrmann, Manaal Faruqui, Bhuwan Dhingra, Diyi Yang, Dipanjan Das
| Challenge: | Existing methods for data-to-text generation often hallucinate phrases not supported by the Wikipedia table. |
| Approach: | They propose a controlled task where annotators directly revise existing Wikipedia sentences to generate a one-sentence description. |
| Outcome: | The proposed task produces a one-sentence description from a Wikipedia table and highlighted cells. |
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 . |
TaTA: A Multilingual Table-to-Text Dataset for African Languages (2023.findings-emnlp)
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Sebastian Gehrmann, Sebastian Ruder, Vitaly Nikolaev, Jan Botha, Michael Chavinda, Ankur Parikh, Clara Rivera
| Challenge: | Existing data-to-text generation datasets are limited to English and a small number of other languages. |
| Approach: | They create the first large multilingual table-to-text dataset with a focus on African languages. |
| Outcome: | The proposed dataset includes 8,700 examples in nine languages including four African languages and a zero-shot test language. |
Towards Continual Learning for Multilingual Machine Translation via Vocabulary Substitution (2021.naacl-main)
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| Challenge: | Existing approaches to multilingual machine translation rely on training models on monolingual data for all languages in a multitask setup. |
| Approach: | They propose a vocabulary adaptation scheme to extend the language capacity of multilingual machine translation models by combining monolingual data with a dictionary. |
| Outcome: | The proposed model improves on existing models by preserving the original model and allowing for competitive performance even with only monolingual data. |
BLEURT: Learning Robust Metrics for Text Generation (2020.acl-main)
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| Challenge: | Text generation has made significant advances, but evaluation metrics have lagged behind. |
| Approach: | They propose a learning evaluation metric for English based on BERT . BLEURT can model human judgment with a few thousand possibly biased training examples . |
| Outcome: | The proposed model can model human judgment with a few thousand potentially biased training examples. |
Reward Gaming in Conditional Text Generation (2023.acl-long)
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| Challenge: | Recent work has used reward functions learned from human annotations to align conditional text generation models with desired behaviors. |
| Approach: | They propose to use reinforcement learning to train conditional text generation models with reward functions learned from human annotations to align outputs with desired behaviors. |
| Outcome: | The proposed framework improves the quality of generated summaries by using saliency and faithfulness metrics. |
Improving Compositional Generalization with Self-Training for Data-to-Text Generation (2022.acl-long)
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| Challenge: | Data-to-text generation focuses on generating fluent natural language responses from structured meaning representations (MRs). |
| Approach: | They propose a template-based input representation that greatly improves the model’s generalization capability. |
| Outcome: | The proposed model improves tree accuracy by 46%+ and reduces slot error rates by 73%+ over the strong baselines on SGD and Weather benchmarks. |