Search Results for author: Semih Yavuz

Found 21 papers, 10 papers with code

Simple Data Augmentation with the Mask Token Improves Domain Adaptation for Dialog Act Tagging

no code implementations EMNLP 2020 Semih Yavuz, Kazuma Hashimoto, Wenhao Liu, Nitish Shirish Keskar, Richard Socher, Caiming Xiong

The concept of Dialogue Act (DA) is universal across different task-oriented dialogue domains - the act of {``}request{''} carries the same speaker intention whether it is for restaurant reservation or flight booking.

Data Augmentation Domain Generalization

Dense Hierarchical Retrieval for Open-Domain Question Answering

1 code implementation Findings (EMNLP) 2021 Ye Liu, Kazuma Hashimoto, Yingbo Zhou, Semih Yavuz, Caiming Xiong, Philip S. Yu

In this work, we propose Dense Hierarchical Retrieval (DHR), a hierarchical framework that can generate accurate dense representations of passages by utilizing both macroscopic semantics in the document and microscopic semantics specific to each passage.

Open-Domain Question Answering

RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering

1 code implementation17 Sep 2021 Xi Ye, Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou, Caiming Xiong

We present RnG-KBQA, a Rank-and-Generate approach for KBQA, which remedies the coverage issue with a generation model while preserving a strong generalization capability.

Entity Linking Knowledge Base Question Answering

Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding

no code implementations Findings (EMNLP) 2021 Shiyang Li, Semih Yavuz, Wenhu Chen, Xifeng Yan

Task-adaptive pre-training (TAPT) and Self-training (ST) have emerged as the major semi-supervised approaches to improve natural language understanding (NLU) tasks with massive amount of unlabeled data.

Named Entity Recognition Natural Language Inference +3

Stage-wise Fine-tuning for Graph-to-Text Generation

1 code implementation ACL 2021 Qingyun Wang, Semih Yavuz, Victoria Lin, Heng Ji, Nazneen Rajani

Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders.

Ranked #3 on Data-to-Text Generation on WebNLG (using extra training data)

Data-to-Text Generation KB-to-Language Generation +1

CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers

1 code implementation ICLR 2021 Shiyang Li, Semih Yavuz, Kazuma Hashimoto, Jia Li, Tong Niu, Nazneen Rajani, Xifeng Yan, Yingbo Zhou, Caiming Xiong

Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations is less understood.

Ranked #2 on Multi-domain Dialogue State Tracking on MULTIWOZ 2.1 (using extra training data)

Dialogue State Tracking Multi-domain Dialogue State Tracking

Unsupervised Paraphrasing with Pretrained Language Models

no code implementations EMNLP 2021 Tong Niu, Semih Yavuz, Yingbo Zhou, Nitish Shirish Keskar, Huan Wang, Caiming Xiong

To enforce a surface form dissimilar from the input, whenever the language model emits a token contained in the source sequence, DB prevents the model from outputting the subsequent source token for the next generation step.

Language Modelling Paraphrase Generation +1

Neural Assistant: Joint Action Prediction, Response Generation, and Latent Knowledge Reasoning

1 code implementation31 Oct 2019 Arvind Neelakantan, Semih Yavuz, Sharan Narang, Vishaal Prasad, Ben Goodrich, Daniel Duckworth, Chinnadhurai Sankar, Xifeng Yan

In this paper, we develop Neural Assistant: a single neural network model that takes conversation history and an external knowledge source as input and jointly produces both text response and action to be taken by the system as output.

Text Generation

Taskmaster-1: Toward a Realistic and Diverse Dialog Dataset

1 code implementation IJCNLP 2019 Bill Byrne, Karthik Krishnamoorthi, Chinnadhurai Sankar, Arvind Neelakantan, Daniel Duckworth, Semih Yavuz, Ben Goodrich, Amit Dubey, Andy Cedilnik, Kyu-Young Kim

A significant barrier to progress in data-driven approaches to building dialog systems is the lack of high quality, goal-oriented conversational data.

DeepCopy: Grounded Response Generation with Hierarchical Pointer Networks

no code implementations WS 2019 Semih Yavuz, Abhinav Rastogi, Guan-Lin Chao, Dilek Hakkani-Tur

Recent advances in neural sequence-to-sequence models have led to promising results for several language generation-based tasks, including dialogue response generation, summarization, and machine translation.

Machine Translation Text Generation +1

Learning Question-Guided Video Representation for Multi-Turn Video Question Answering

no code implementations WS 2019 Guan-Lin Chao, Abhinav Rastogi, Semih Yavuz, Dilek Hakkani-Tür, Jindong Chen, Ian Lane

Understanding and conversing about dynamic scenes is one of the key capabilities of AI agents that navigate the environment and convey useful information to humans.

Question Answering Text Generation +1

Monotonic Infinite Lookback Attention for Simultaneous Machine Translation

no code implementations ACL 2019 Naveen Arivazhagan, Colin Cherry, Wolfgang Macherey, Chung-Cheng Chiu, Semih Yavuz, Ruoming Pang, Wei Li, Colin Raffel

Simultaneous machine translation begins to translate each source sentence before the source speaker is finished speaking, with applications to live and streaming scenarios.

Machine Translation Translation

Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling

3 code implementations21 Feb 2019 Jonathan Shen, Patrick Nguyen, Yonghui Wu, Zhifeng Chen, Mia X. Chen, Ye Jia, Anjuli Kannan, Tara Sainath, Yuan Cao, Chung-Cheng Chiu, Yanzhang He, Jan Chorowski, Smit Hinsu, Stella Laurenzo, James Qin, Orhan Firat, Wolfgang Macherey, Suyog Gupta, Ankur Bapna, Shuyuan Zhang, Ruoming Pang, Ron J. Weiss, Rohit Prabhavalkar, Qiao Liang, Benoit Jacob, Bowen Liang, HyoukJoong Lee, Ciprian Chelba, Sébastien Jean, Bo Li, Melvin Johnson, Rohan Anil, Rajat Tibrewal, Xiaobing Liu, Akiko Eriguchi, Navdeep Jaitly, Naveen Ari, Colin Cherry, Parisa Haghani, Otavio Good, Youlong Cheng, Raziel Alvarez, Isaac Caswell, Wei-Ning Hsu, Zongheng Yang, Kuan-Chieh Wang, Ekaterina Gonina, Katrin Tomanek, Ben Vanik, Zelin Wu, Llion Jones, Mike Schuster, Yanping Huang, Dehao Chen, Kazuki Irie, George Foster, John Richardson, Klaus Macherey, Antoine Bruguier, Heiga Zen, Colin Raffel, Shankar Kumar, Kanishka Rao, David Rybach, Matthew Murray, Vijayaditya Peddinti, Maxim Krikun, Michiel A. U. Bacchiani, Thomas B. Jablin, Rob Suderman, Ian Williams, Benjamin Lee, Deepti Bhatia, Justin Carlson, Semih Yavuz, Yu Zhang, Ian McGraw, Max Galkin, Qi Ge, Golan Pundak, Chad Whipkey, Todd Wang, Uri Alon, Dmitry Lepikhin, Ye Tian, Sara Sabour, William Chan, Shubham Toshniwal, Baohua Liao, Michael Nirschl, Pat Rondon

Lingvo is a Tensorflow framework offering a complete solution for collaborative deep learning research, with a particular focus towards sequence-to-sequence models.

Sequence-To-Sequence Speech Recognition

CaLcs: Continuously Approximating Longest Common Subsequence for Sequence Level Optimization

no code implementations EMNLP 2018 Semih Yavuz, Chung-Cheng Chiu, Patrick Nguyen, Yonghui Wu

Maximum-likelihood estimation (MLE) is one of the most widely used approaches for training structured prediction models for text-generation based natural language processing applications.

Abstractive Text Summarization Image Captioning +5

What It Takes to Achieve 100\% Condition Accuracy on WikiSQL

no code implementations EMNLP 2018 Semih Yavuz, Izzeddin Gur, Yu Su, Xifeng Yan

The SQL queries in WikiSQL are simple: Each involves one relation and does not have any join operation.

Translation

DialSQL: Dialogue Based Structured Query Generation

no code implementations ACL 2018 Izzeddin Gur, Semih Yavuz, Yu Su, Xifeng Yan

The recent advance in deep learning and semantic parsing has significantly improved the translation accuracy of natural language questions to structured queries.

Semantic Parsing Translation

Recovering Question Answering Errors via Query Revision

no code implementations EMNLP 2017 Semih Yavuz, Izzeddin Gur, Yu Su, Xifeng Yan

The existing factoid QA systems often lack a post-inspection component that can help models recover from their own mistakes.

Question Answering Semantic Parsing

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