2.2k citations · 10.9k across the 144 of their papers we have counts for
36 papers · 1 filter
Few-Shot Named Entity Recognition: A Comprehensive Study
Jiaxin Huang, Chunyuan Li, Krishan Subudhi +6
This paper presents a comprehensive study to efficiently build named entity recognition (NER) systems when a small number of in-domain labeled data is available. Based upon recent…
RADDLE: An Evaluation Benchmark and Analysis Platform for Robust Task-oriented Dialog Systems
Baolin Peng, Chunyuan Li, Zhu Zhang +3
For task-oriented dialog systems to be maximally useful, it must be able to process conversations in a way that is (1) generalizable with a small number of training examples for ne…
MiniVLM: A Smaller and Faster Vision-Language Model
Jianfeng Wang, Xiaowei Hu, Pengchuan Zhang +5
Recent vision-language (VL) studies have shown remarkable progress by learning generic representations from massive image-text pairs with transformer models and then fine-tuning on…
Neuro-Symbolic Representations for Video Captioning: A Case for Leveraging Inductive Biases for Vision and Language
Hassan Akbari, Hamid Palangi, Jianwei Yang +6
Neuro-symbolic representations have proved effective in learning structure information in vision and language. In this paper, we propose a new model architecture for learning multi…
Overview of the Ninth Dialog System Technology Challenge: DSTC9
Chulaka Gunasekara, Seokhwan Kim, Luis Fernando D'Haro +36
This paper introduces the Ninth Dialog System Technology Challenge (DSTC-9). This edition of the DSTC focuses on applying end-to-end dialog technologies for four distinct tasks in…
CMT in TREC-COVID Round 2: Mitigating the Generalization Gaps from Web to Special Domain Search
Chenyan Xiong, Zhenghao Liu, Si Sun +7
Neural rankers based on deep pretrained language models (LMs) have been shown to improve many information retrieval benchmarks. However, these methods are affected by their the cor…
Text Editing by Command
Felix Faltings, Michel Galley, Gerold Hintz +4
A prevailing paradigm in neural text generation is one-shot generation, where text is produced in a single step. The one-shot setting is inadequate, however, when the constraints t…
MagGAN: High-Resolution Face Attribute Editing with Mask-Guided Generative Adversarial Network
Yi Wei, Zhe Gan, Wenbo Li +5
We present Mask-guided Generative Adversarial Network (MagGAN) for high-resolution face attribute editing, in which semantic facial masks from a pre-trained face parser are used to…
GO FIGURE: A Meta Evaluation of Factuality in Summarization
Saadia Gabriel, Asli Celikyilmaz, Rahul Jha +2
While neural language models can generate text with remarkable fluency and coherence, controlling for factual correctness in generation remains an open research question. This majo…
Posterior Differential Regularization with f-divergence for Improving Model Robustness
Hao Cheng, Xiaodong Liu, Lis Pereira +2
We address the problem of enhancing model robustness through regularization. Specifically, we focus on methods that regularize the model posterior difference between clean and nois…
Robust Conversational AI with Grounded Text Generation
Jianfeng Gao, Baolin Peng, Chunyuan Li +4
This article presents a hybrid approach based on a Grounded Text Generation (GTG) model to building robust task bots at scale. GTG is a hybrid model which uses a large-scale Transf…
VIVO: Visual Vocabulary Pre-Training for Novel Object Captioning
Xiaowei Hu, Xi Yin, Kevin Lin +4
It is highly desirable yet challenging to generate image captions that can describe novel objects which are unseen in caption-labeled training data, a capability that is evaluated…
Generation-Augmented Retrieval for Open-domain Question Answering
Yuning Mao, Pengcheng He, Xiaodong Liu +4
We propose Generation-Augmented Retrieval (GAR) for answering open-domain questions, which augments a query through text generation of heuristically discovered relevant contexts wi…
HittER: Hierarchical Transformers for Knowledge Graph Embeddings
Sanxing Chen, Xiaodong Liu, Jianfeng Gao +3
This paper examines the challenging problem of learning representations of entities and relations in a complex multi-relational knowledge graph. We propose HittER, a Hierarchical T…
Very Deep Transformers for Neural Machine Translation
Xiaodong Liu, Kevin Duh, Liyuan Liu +1
We explore the application of very deep Transformer models for Neural Machine Translation (NMT). Using a simple yet effective initialization technique that stabilizes training, we…
Domain-Specific Language Model Pretraining for Biomedical Natural Language Processing
Yu Gu, Robert Tinn, Hao Cheng +6
Pretraining large neural language models, such as BERT, has led to impressive gains on many natural language processing (NLP) tasks. However, most pretraining efforts focus on gene…
Few-Shot Generative Conversational Query Rewriting
Shi Yu, Jiahua Liu, Jingqin Yang +4
Conversational query rewriting aims to reformulate a concise conversational query to a fully specified, context-independent query that can be effectively handled by existing inform…
DeBERTa: Decoding-enhanced BERT with Disentangled Attention
Pengcheng He, Xiaodong Liu, Jianfeng Gao +1
Recent progress in pre-trained neural language models has significantly improved the performance of many natural language processing (NLP) tasks. In this paper we propose a new mod…
Novel Human-Object Interaction Detection via Adversarial Domain Generalization
Yuhang Song, Wenbo Li, Lei Zhang +6
We study in this paper the problem of novel human-object interaction (HOI) detection, aiming at improving the generalization ability of the model to unseen scenarios. The challenge…
Is Your Goal-Oriented Dialog Model Performing Really Well? Empirical Analysis of System-wise Evaluation
Ryuichi Takanobu, Qi Zhu, Jinchao Li +3
There is a growing interest in developing goal-oriented dialog systems which serve users in accomplishing complex tasks through multi-turn conversations. Although many methods are…
SOLOIST: Building Task Bots at Scale with Transfer Learning and Machine Teaching
Baolin Peng, Chunyuan Li, Jinchao Li +3
We present a new method SOLOIST that uses transfer learning and machine teaching to build task bots at scale. We parameterize classical modular task-oriented dialog systems using a…
RMM: A Recursive Mental Model for Dialog Navigation
Homero Roman Roman, Yonatan Bisk, Jesse Thomason +2
Language-guided robots must be able to both ask humans questions and understand answers. Much existing work focuses only on the latter. In this paper, we go beyond instruction foll…
A Controllable Model of Grounded Response Generation
Zeqiu Wu, Michel Galley, Chris Brockett +8
Current end-to-end neural conversation models inherently lack the flexibility to impose semantic control in the response generation process, often resulting in uninteresting respon…
Learning Compliance Adaptation in Contact-Rich Manipulation
Jianfeng Gao, You Zhou, Tamim Asfour
Compliant robot behavior is crucial for the realization of contact-rich manipulation tasks. In such tasks, it is important to ensure a high stiffness and force tracking accuracy du…