146 citations · 565 across the 30 of their papers we have counts for
31 papers
Exploring the Viability of Synthetic Query Generation for Relevance Prediction
Aditi Chaudhary, Karthik Raman, Krishna Srinivasan +3
Query-document relevance prediction is a critical problem in Information Retrieval systems. This problem has increasingly been tackled using (pretrained) transformer-based models w…
How Does Beam Search improve Span-Level Confidence Estimation in Generative Sequence Labeling?
Kazuma Hashimoto, Iftekhar Naim, Karthik Raman
Sequence labeling is a core task in text understanding for IE/IR systems. Text generation models have increasingly become the go-to solution for such tasks (e.g., entity extraction…
GROOT: Corrective Reward Optimization for Generative Sequential Labeling
Kazuma Hashimoto, Karthik Raman
Sequential labeling is a fundamental NLP task, forming the backbone of many applications. Supervised learning of Seq2Seq models has shown great success on these problems. However,…
Improving the Faithfulness of Abstractive Summarization via Entity Coverage Control
Haopeng Zhang, Semih Yavuz, Wojciech Kryscinski +2
Abstractive summarization systems leveraging pre-training language models have achieved superior results on benchmark datasets. However, such models have been shown to be more pron…
Modeling Multi-hop Question Answering as Single Sequence Prediction
Semih Yavuz, Kazuma Hashimoto, Yingbo Zhou +2
Fusion-in-decoder (Fid) (Izacard and Grave, 2020) is a generative question answering (QA) model that leverages passage retrieval with a pre-trained transformer and pushed the state…
OneAligner: Zero-shot Cross-lingual Transfer with One Rich-Resource Language Pair for Low-Resource Sentence Retrieval
Tong Niu, Kazuma Hashimoto, Yingbo Zhou +1
Aligning parallel sentences in multilingual corpora is essential to curating data for downstream applications such as Machine Translation. In this work, we present OneAligner, an a…
Choose Your QA Model Wisely: A Systematic Study of Generative and Extractive Readers for Question Answering
Man Luo, Kazuma Hashimoto, Semih Yavuz +3
While both extractive and generative readers have been successfully applied to the Question Answering (QA) task, little attention has been paid toward the systematic comparison of…
Converse: A Tree-Based Modular Task-Oriented Dialogue System
Tian Xie, Xinyi Yang, Angela S. Lin +13
Creating a system that can have meaningful conversations with humans to help accomplish tasks is one of the ultimate goals of Artificial Intelligence (AI). It has defined the meani…
Transforming Sequence Tagging Into A Seq2Seq Task
Karthik Raman, Iftekhar Naim, Jiecao Chen +3
Pretrained, large, generative language models (LMs) have had great success in a wide range of sequence tagging and structured prediction tasks. Casting a sequence tagging task as a…
Dense Hierarchical Retrieval for Open-Domain Question Answering
Ye Liu, Kazuma Hashimoto, Yingbo Zhou +3
Dense neural text retrieval has achieved promising results on open-domain Question Answering (QA), where latent representations of questions and passages are exploited for maximum…
Field Extraction from Forms with Unlabeled Data
Mingfei Gao, Zeyuan Chen, Nikhil Naik +3
We propose a novel framework to conduct field extraction from forms with unlabeled data. To bootstrap the training process, we develop a rule-based method for mining noisy pseudo-l…
RnG-KBQA: Generation Augmented Iterative Ranking for Knowledge Base Question Answering
Xi Ye, Semih Yavuz, Kazuma Hashimoto +2
Existing KBQA approaches, despite achieving strong performance on i.i.d. test data, often struggle in generalizing to questions involving unseen KB schema items. Prior ranking-base…
Are Pretrained Transformers Robust in Intent Classification? A Missing Ingredient in Evaluation of Out-of-Scope Intent Detection
Jianguo Zhang, Kazuma Hashimoto, Yao Wan +4
Pre-trained Transformer-based models were reported to be robust in intent classification. In this work, we first point out the importance of in-domain out-of-scope detection in few…
Focused Attention Improves Document-Grounded Generation
Shrimai Prabhumoye, Kazuma Hashimoto, Yingbo Zhou +2
Document grounded generation is the task of using the information provided in a document to improve text generation. This work focuses on two different document grounded generation…
Causal-aware Safe Policy Improvement for Task-oriented dialogue
Govardana Sachithanandam Ramachandran, Kazuma Hashimoto, Caiming Xiong
The recent success of reinforcement learning's (RL) in solving complex tasks is most often attributed to its capacity to explore and exploit an environment where it has been traine…
Neural Text Generation with Artificial Negative Examples
Keisuke Shirai, Kazuma Hashimoto, Akiko Eriguchi +2
Neural text generation models conditioning on given input (e.g. machine translation and image captioning) are usually trained by maximum likelihood estimation of target text. Howev…
Discriminative Nearest Neighbor Few-Shot Intent Detection by Transferring Natural Language Inference
Jian-Guo Zhang, Kazuma Hashimoto, Wenhao Liu +5
Intent detection is one of the core components of goal-oriented dialog systems, and detecting out-of-scope (OOS) intents is also a practically important skill. Few-shot learning is…
CoCo: Controllable Counterfactuals for Evaluating Dialogue State Trackers
Shiyang Li, Semih Yavuz, Kazuma Hashimoto +6
Dialogue state trackers have made significant progress on benchmark datasets, but their generalization capability to novel and realistic scenarios beyond the held-out conversations…
A High-Quality Multilingual Dataset for Structured Documentation Translation
Kazuma Hashimoto, Raffaella Buschiazzo, James Bradbury +3
This paper presents a high-quality multilingual dataset for the documentation domain to advance research on localization of structured text. Unlike widely-used datasets for transla…
CO-Search: COVID-19 Information Retrieval with Semantic Search, Question Answering, and Abstractive Summarization
Andre Esteva, Anuprit Kale, Romain Paulus +4
The COVID-19 global pandemic has resulted in international efforts to understand, track, and mitigate the disease, yielding a significant corpus of COVID-19 and SARS-CoV-2-related…
Adv-BERT: BERT is not robust on misspellings! Generating nature adversarial samples on BERT
Lichao Sun, Kazuma Hashimoto, Wenpeng Yin +4
There is an increasing amount of literature that claims the brittleness of deep neural networks in dealing with adversarial examples that are created maliciously. It is unclear, ho…
Learning to Retrieve Reasoning Paths over Wikipedia Graph for Question Answering
Akari Asai, Kazuma Hashimoto, Hannaneh Hajishirzi +2
Answering questions that require multi-hop reasoning at web-scale necessitates retrieving multiple evidence documents, one of which often has little lexical or semantic relationshi…
Find or Classify? Dual Strategy for Slot-Value Predictions on Multi-Domain Dialog State Tracking
Jian-Guo Zhang, Kazuma Hashimoto, Chien-Sheng Wu +4
Dialog state tracking (DST) is a core component in task-oriented dialog systems. Existing approaches for DST mainly fall into one of two categories, namely, ontology-based and onto…
Multilingual Extractive Reading Comprehension by Runtime Machine Translation
Akari Asai, Akiko Eriguchi, Kazuma Hashimoto +1
Despite recent work in Reading Comprehension (RC), progress has been mostly limited to English due to the lack of large-scale datasets in other languages. In this work, we introduc…