674 citations
Counting papers with at most 25 authors, so collaboration author lists do not stand in for the people here.
- University of Illinois ChicagoUS13 papers
- Singapore Management UniversitySG11 papers
- Carnegie Mellon UniversityUS8 papers
- Georgia Institute of TechnologyUS7 papers
- Princeton UniversityUS7 papers
- Nanyang Technological UniversitySG6 papers
- University of Illinois Urbana-ChampaignUS6 papers
- Agency for Science, Technology and ResearchSG5 papers
- Chinese University of Hong KongHK5 papers
- University of Maryland, College ParkUS5 papers
- University of WashingtonUS5 papers
- Beihang UniversityCN4 papers
54 papers · 1 filter
Automatic Bug Detection in LLM-Powered Text-Based Games Using LLMs
Claire Jin, Sudha Rao, Xiangyu Peng +4
Advancements in large language models (LLMs) are revolutionizing interactive game design, enabling dynamic plotlines and interactions between players and non-player characters (NPC…
A Data-centric Framework for Improving Domain-specific Machine Reading Comprehension Datasets
Iva Bojic, Josef Halim, Verena Suharman +6
Low-quality data can cause downstream problems in high-stakes applications. Data-centric approach emphasizes on improving dataset quality to enhance model performance. High-quality…
Cascaded Fast and Slow Models for Efficient Semantic Code Search
Akhilesh Deepak Gotmare, Junnan Li, Shafiq Joty +1
The goal of natural language semantic code search is to retrieve a semantically relevant code snippet from a fixed set of candidates using a natural language query. Existing approa…
Multimodal Conditionality for Natural Language Generation
Michael Sollami, Aashish Jain
Large scale pretrained language models have demonstrated state-of-the-art performance in language understanding tasks. Their application has recently expanded into multimodality le…
Task-adaptive Pre-training and Self-training are Complementary for Natural Language Understanding
Shiyang Li, Semih Yavuz, Wenhu Chen +1
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 amo…
Multi-Pair Text Style Transfer on Unbalanced Data
Xing Han, Jessica Lundin
Text-style transfer aims to convert text given in one domain into another by paraphrasing the sentence or substituting the keywords without altering the content. By necessity, stat…
DocNLI: A Large-scale Dataset for Document-level Natural Language Inference
Wenpeng Yin, Dragomir Radev, Caiming Xiong
Natural language inference (NLI) is formulated as a unified framework for solving various NLP problems such as relation extraction, question answering, summarization, etc. It has b…
Controllable Abstractive Dialogue Summarization with Sketch Supervision
Chien-Sheng Wu, Linqing Liu, Wenhao Liu +2
In this paper, we aim to improve abstractive dialogue summarization quality and, at the same time, enable granularity control. Our model has two primary components and stages: 1) a…
Stage-wise Fine-tuning for Graph-to-Text Generation
Qingyun Wang, Semih Yavuz, Victoria Lin +2
Graph-to-text generation has benefited from pre-trained language models (PLMs) in achieving better performance than structured graph encoders. However, they fail to fully utilize t…
Pseudo Siamese Network for Few-shot Intent Generation
Congying Xia, Caiming Xiong, Philip Yu
Few-shot intent detection is a challenging task due to the scare annotation problem. In this paper, we propose a Pseudo Siamese Network (PSN) to generate labeled data for few-shot…
Learning to Synthesize Data for Semantic Parsing
Bailin Wang, Wenpeng Yin, Xi Victoria Lin +1
Synthesizing data for semantic parsing has gained increasing attention recently. However, most methods require handcrafted (high-precision) rules in their generative process, hinde…
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…
Code-Mixing on Sesame Street: Dawn of the Adversarial Polyglots
Samson Tan, Shafiq Joty
Multilingual models have demonstrated impressive cross-lingual transfer performance. However, test sets like XNLI are monolingual at the example level. In multilingual communities,…
Open-Retrieval Conversational Machine Reading
Yifan Gao, Jingjing Li, Chien-Sheng Wu +2
In conversational machine reading, systems need to interpret natural language rules, answer high-level questions such as "May I qualify for VA health care benefits?", and ask follo…
Weakly Supervised Neuro-Symbolic Module Networks for Numerical Reasoning
Amrita Saha, Shafiq Joty, Steven C. H. Hoi
Neural Module Networks (NMNs) have been quite successful in incorporating explicit reasoning as learnable modules in various question answering tasks, including the most generic fo…
Bridging Textual and Tabular Data for Cross-Domain Text-to-SQL Semantic Parsing
Xi Victoria Lin, Richard Socher, Caiming Xiong
We present BRIDGE, a powerful sequential architecture for modeling dependencies between natural language questions and relational databases in cross-DB semantic parsing. BRIDGE rep…
Adversarial Meta Sampling for Multilingual Low-Resource Speech Recognition
Yubei Xiao, Ke Gong, Pan Zhou +3
Low-resource automatic speech recognition (ASR) is challenging, as the low-resource target language data cannot well train an ASR model. To solve this issue, meta-learning formulat…
Improving Limited Labeled Dialogue State Tracking with Self-Supervision
Chien-Sheng Wu, Steven Hoi, Caiming Xiong
Existing dialogue state tracking (DST) models require plenty of labeled data. However, collecting high-quality labels is costly, especially when the number of domains increases. In…
Probing Task-Oriented Dialogue Representation from Language Models
Chien-Sheng Wu, Caiming Xiong
This paper investigates pre-trained language models to find out which model intrinsically carries the most informative representation for task-oriented dialogue tasks. We approach…
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…
GeDi: Generative Discriminator Guided Sequence Generation
Ben Krause, Akhilesh Deepak Gotmare, Bryan McCann +4
While large-scale language models (LMs) are able to imitate the distribution of natural language well enough to generate realistic text, it is difficult to control which regions of…
ColloQL: Robust Cross-Domain Text-to-SQL Over Search Queries
Karthik Radhakrishnan, Arvind Srikantan, Xi Victoria Lin
Translating natural language utterances to executable queries is a helpful technique in making the vast amount of data stored in relational databases accessible to a wider range of…
Explaining and Improving Model Behavior with k Nearest Neighbor Representations
Nazneen Fatema Rajani, Ben Krause, Wengpeng Yin +3
Interpretability techniques in NLP have mainly focused on understanding individual predictions using attention visualization or gradient-based saliency maps over tokens. We propose…
Discern: Discourse-Aware Entailment Reasoning Network for Conversational Machine Reading
Yifan Gao, Chien-Sheng Wu, Jingjing Li +5
Document interpretation and dialog understanding are the two major challenges for conversational machine reading. In this work, we propose Discern, a discourse-aware entailment rea…