no code implementations • 3 Aug 2024 • Yanfei Chen, Jinsung Yoon, Devendra Singh Sachan, Qingze Wang, Vincent Cohen-Addad, Mohammadhossein Bateni, Chen-Yu Lee, Tomas Pfister
Recent advances in large language models (LLMs) have enabled autonomous agents with complex reasoning and task-fulfillment capabilities using a wide range of tools.
1 code implementation • 19 Jun 2024 • Jinhyuk Lee, Anthony Chen, Zhuyun Dai, Dheeru Dua, Devendra Singh Sachan, Michael Boratko, Yi Luan, Sébastien M. R. Arnold, Vincent Perot, Siddharth Dalmia, Hexiang Hu, Xudong Lin, Panupong Pasupat, Aida Amini, Jeremy R. Cole, Sebastian Riedel, Iftekhar Naim, Ming-Wei Chang, Kelvin Guu
Long-context language models (LCLMs) have the potential to revolutionize our approach to tasks traditionally reliant on external tools like retrieval systems or databases.
1 code implementation • 21 Jun 2022 • Devendra Singh Sachan, Mike Lewis, Dani Yogatama, Luke Zettlemoyer, Joelle Pineau, Manzil Zaheer
We introduce ART, a new corpus-level autoencoding approach for training dense retrieval models that does not require any labeled training data.
1 code implementation • 15 Apr 2022 • Devendra Singh Sachan, Mike Lewis, Mandar Joshi, Armen Aghajanyan, Wen-tau Yih, Joelle Pineau, Luke Zettlemoyer
We propose a simple and effective re-ranking method for improving passage retrieval in open question answering.
2 code implementations • NeurIPS 2021 • Devendra Singh Sachan, Siva Reddy, William Hamilton, Chris Dyer, Dani Yogatama
We model retrieval decisions as latent variables over sets of relevant documents.
2 code implementations • ACL 2021 • Devendra Singh Sachan, Mostofa Patwary, Mohammad Shoeybi, Neel Kant, Wei Ping, William L Hamilton, Bryan Catanzaro
We also explore two approaches for end-to-end supervised training of the reader and retriever components in OpenQA models.
no code implementations • 22 Oct 2020 • Devendra Singh Sachan, Lingfei Wu, Mrinmaya Sachan, William Hamilton
In this work, we introduce a series of strong transformer models for multi-hop question generation, including a graph-augmented transformer that leverages relations between entities in the text.
1 code implementation • AAAI 2019 2019 • Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
In this paper, we study bidirectional LSTM network for the task of text classification using both supervised and semi-supervised approaches.
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1 code implementation • EACL 2021 • Devendra Singh Sachan, Yuhao Zhang, Peng Qi, William Hamilton
Our empirical analysis demonstrates that these syntax-infused transformers obtain state-of-the-art results on SRL and relation extraction tasks.
4 code implementations • ACL 2019 • Zhiting Hu, Haoran Shi, Bowen Tan, Wentao Wang, Zichao Yang, Tiancheng Zhao, Junxian He, Lianhui Qin, Di Wang, Xuezhe Ma, Zhengzhong Liu, Xiaodan Liang, Wangrong Zhu, Devendra Singh Sachan, Eric P. Xing
The versatile toolkit also fosters technique sharing across different text generation tasks.
1 code implementation • WS 2018 • Devendra Singh Sachan, Graham Neubig
In multilingual neural machine translation, it has been shown that sharing a single translation model between multiple languages can achieve competitive performance, sometimes even leading to performance gains over bilingually trained models.
1 code implementation • NAACL 2018 • Ye Qi, Devendra Singh Sachan, Matthieu Felix, Sarguna Janani Padmanabhan, Graham Neubig
The performance of Neural Machine Translation (NMT) systems often suffers in low-resource scenarios where sufficiently large-scale parallel corpora cannot be obtained.
1 code implementation • WS 2018 • Graham Neubig, Matthias Sperber, Xinyi Wang, Matthieu Felix, Austin Matthews, Sarguna Padmanabhan, Ye Qi, Devendra Singh Sachan, Philip Arthur, Pierre Godard, John Hewitt, Rachid Riad, Liming Wang
In this paper we describe the design of XNMT and its experiment configuration system, and demonstrate its utility on the tasks of machine translation, speech recognition, and multi-tasked machine translation/parsing.
1 code implementation • COLING 2018 • Devendra Singh Sachan, Manzil Zaheer, Ruslan Salakhutdinov
Text classification is one of the most widely studied tasks in natural language processing.
2 code implementations • 21 Nov 2017 • Devendra Singh Sachan, Pengtao Xie, Mrinmaya Sachan, Eric P. Xing
We also show that BiLM weight transfer leads to a faster model training and the pretrained model requires fewer training examples to achieve a particular F1 score.
no code implementations • 2 Aug 2015 • Devendra Singh Sachan, Shailesh Kumar
Distributed representations of words and paragraphs as semantic embeddings in high dimensional data are used across a number of Natural Language Understanding tasks such as retrieval, translation, and classification.