Search Results for author: Vikas Raunak

Found 13 papers, 6 papers with code

SALTED: A Framework for SAlient Long-Tail Translation Error Detection

no code implementations20 May 2022 Vikas Raunak, Matt Post, Arul Menezes

Traditional machine translation (MT) metrics provide an average measure of translation quality that is insensitive to the long tail of behavioral problems in MT.

Machine Translation Translation

NL-Augmenter: A Framework for Task-Sensitive Natural Language Augmentation

1 code implementation6 Dec 2021 Kaustubh D. Dhole, Varun Gangal, Sebastian Gehrmann, Aadesh Gupta, Zhenhao Li, Saad Mahamood, Abinaya Mahendiran, Simon Mille, Ashish Srivastava, Samson Tan, Tongshuang Wu, Jascha Sohl-Dickstein, Jinho D. Choi, Eduard Hovy, Ondrej Dusek, Sebastian Ruder, Sajant Anand, Nagender Aneja, Rabin Banjade, Lisa Barthe, Hanna Behnke, Ian Berlot-Attwell, Connor Boyle, Caroline Brun, Marco Antonio Sobrevilla Cabezudo, Samuel Cahyawijaya, Emile Chapuis, Wanxiang Che, Mukund Choudhary, Christian Clauss, Pierre Colombo, Filip Cornell, Gautier Dagan, Mayukh Das, Tanay Dixit, Thomas Dopierre, Paul-Alexis Dray, Suchitra Dubey, Tatiana Ekeinhor, Marco Di Giovanni, Rishabh Gupta, Louanes Hamla, Sang Han, Fabrice Harel-Canada, Antoine Honore, Ishan Jindal, Przemyslaw K. Joniak, Denis Kleyko, Venelin Kovatchev, Kalpesh Krishna, Ashutosh Kumar, Stefan Langer, Seungjae Ryan Lee, Corey James Levinson, Hualou Liang, Kaizhao Liang, Zhexiong Liu, Andrey Lukyanenko, Vukosi Marivate, Gerard de Melo, Simon Meoni, Maxime Meyer, Afnan Mir, Nafise Sadat Moosavi, Niklas Muennighoff, Timothy Sum Hon Mun, Kenton Murray, Marcin Namysl, Maria Obedkova, Priti Oli, Nivranshu Pasricha, Jan Pfister, Richard Plant, Vinay Prabhu, Vasile Pais, Libo Qin, Shahab Raji, Pawan Kumar Rajpoot, Vikas Raunak, Roy Rinberg, Nicolas Roberts, Juan Diego Rodriguez, Claude Roux, Vasconcellos P. H. S., Ananya B. Sai, Robin M. Schmidt, Thomas Scialom, Tshephisho Sefara, Saqib N. Shamsi, Xudong Shen, Haoyue Shi, Yiwen Shi, Anna Shvets, Nick Siegel, Damien Sileo, Jamie Simon, Chandan Singh, Roman Sitelew, Priyank Soni, Taylor Sorensen, William Soto, Aman Srivastava, KV Aditya Srivatsa, Tony Sun, Mukund Varma T, A Tabassum, Fiona Anting Tan, Ryan Teehan, Mo Tiwari, Marie Tolkiehn, Athena Wang, Zijian Wang, Gloria Wang, Zijie J. Wang, Fuxuan Wei, Bryan Wilie, Genta Indra Winata, Xinyi Wu, Witold Wydmański, Tianbao Xie, Usama Yaseen, M. Yee, Jing Zhang, Yue Zhang

Data augmentation is an important component in the robustness evaluation of models in natural language processing (NLP) and in enhancing the diversity of the data they are trained on.

Data Augmentation

Searchable Hidden Intermediates for End-to-End Models of Decomposable Sequence Tasks

no code implementations NAACL 2021 Siddharth Dalmia, Brian Yan, Vikas Raunak, Florian Metze, Shinji Watanabe

In this work, we present an end-to-end framework that exploits compositionality to learn searchable hidden representations at intermediate stages of a sequence model using decomposed sub-tasks.

Speech Recognition Translation

The Curious Case of Hallucinations in Neural Machine Translation

1 code implementation NAACL 2021 Vikas Raunak, Arul Menezes, Marcin Junczys-Dowmunt

In this work, we study hallucinations in Neural Machine Translation (NMT), which lie at an extreme end on the spectrum of NMT pathologies.

Knowledge Distillation Machine Translation +1

Ranking Clarification Questions via Natural Language Inference

no code implementations18 Aug 2020 Vaibhav Kumar, Vikas Raunak, Jamie Callan

Given a natural language query, teaching machines to ask clarifying questions is of immense utility in practical natural language processing systems.

Natural Language Inference Reading Comprehension

On Compositionality in Neural Machine Translation

no code implementations4 Nov 2019 Vikas Raunak, Vaibhav Kumar, Florian Metze

We investigate two specific manifestations of compositionality in Neural Machine Translation (NMT) : (1) Productivity - the ability of the model to extend its predictions beyond the observed length in training data and (2) Systematicity - the ability of the model to systematically recombine known parts and rules.

Machine Translation Translation

On Leveraging the Visual Modality for Neural Machine Translation

no code implementations WS 2019 Vikas Raunak, Sang Keun Choe, Quanyang Lu, Yi Xu, Florian Metze

Leveraging the visual modality effectively for Neural Machine Translation (NMT) remains an open problem in computational linguistics.

Multimodal Machine Translation Translation

On Dimensional Linguistic Properties of the Word Embedding Space

2 code implementations WS 2020 Vikas Raunak, Vaibhav Kumar, Vivek Gupta, Florian Metze

Word embeddings have become a staple of several natural language processing tasks, yet much remains to be understood about their properties.

Machine Translation Sentence Classification +2

Effective Dimensionality Reduction for Word Embeddings

1 code implementation WS 2019 Vikas Raunak, Vivek Gupta, Florian Metze

Pre-trained word embeddings are used in several downstream applications as well as for constructing representations for sentences, paragraphs and documents.

Dimensionality Reduction Word Embeddings

Learned In Speech Recognition: Contextual Acoustic Word Embeddings

no code implementations18 Feb 2019 Shruti Palaskar, Vikas Raunak, Florian Metze

End-to-end acoustic-to-word speech recognition models have recently gained popularity because they are easy to train, scale well to large amounts of training data, and do not require a lexicon.

Speech Recognition Spoken Language Understanding +1

Simple and Effective Dimensionality Reduction for Word Embeddings

1 code implementation11 Aug 2017 Vikas Raunak

Word embeddings have become the basic building blocks for several natural language processing and information retrieval tasks.

Dimensionality Reduction Information Retrieval +2

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