20 citations · 71 across the 20 of their papers we have counts for
20 papers
KBQA-o1: Agentic Knowledge Base Question Answering with Monte Carlo Tree Search
Haoran Luo, Haihong E, Yikai Guo +7
Knowledge Base Question Answering (KBQA) aims to answer natural language questions with a large-scale structured knowledge base (KB). Despite advancements with large language model…
Efficiently Aligned Cross-Lingual Transfer Learning for Conversational Tasks using Prompt-Tuning
Lifu Tu, Jin Qu, Semih Yavuz +4
Cross-lingual transfer of language models trained on high-resource languages like English has been widely studied for many NLP tasks, but focus on conversational tasks has been rat…
Conformal Predictor for Improving Zero-shot Text Classification Efficiency
Prafulla Kumar Choubey, Yu Bai, Chien-Sheng Wu +2
Pre-trained language models (PLMs) have been shown effective for zero-shot (0shot) text classification. 0shot models based on natural language inference (NLI) and next sentence pre…
Marvista: Exploring the Design of a Human-AI Collaborative News Reading Tool
Xiang 'Anthony' Chen, Chien-Sheng Wu, Lidiya Murakhovs'ka +4
We explore the design of Marvista -- a human-AI collaborative tool that employs a suite of natural language processing models to provide end-to-end support for reading online news…
Quiz Design Task: Helping Teachers Create Quizzes with Automated Question Generation
Philippe Laban, Chien-Sheng Wu, Lidiya Murakhovs'ka +2
Question generation (QGen) models are often evaluated with standardized NLG metrics that are based on n-gram overlap. In this paper, we measure whether these metric improvements tr…
Near-Negative Distinction: Giving a Second Life to Human Evaluation Datasets
Philippe Laban, Chien-Sheng Wu, Wenhao Liu +1
Precisely assessing the progress in natural language generation (NLG) tasks is challenging, and human evaluation to establish a preference in a model's output over another is often…
A Generative Language Model for Few-shot Aspect-Based Sentiment Analysis
Ehsan Hosseini-Asl, Wenhao Liu, Caiming Xiong
Sentiment analysis is an important task in natural language processing. In recent works, pre-trained language models are often used to achieve state-of-the-art results, especially…
Structure Extraction in Task-Oriented Dialogues with Slot Clustering
Liang Qiu, Chien-Sheng Wu, Wenhao Liu +1
Extracting structure information from dialogue data can help us better understand user and system behaviors. In task-oriented dialogues, dialogue structure has often been considere…
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…
QAFactEval: Improved QA-Based Factual Consistency Evaluation for Summarization
Alexander R. Fabbri, Chien-Sheng Wu, Wenhao Liu +1
Factual consistency is an essential quality of text summarization models in practical settings. Existing work in evaluating this dimension can be broadly categorized into two lines…
Open Vocabulary Object Detection with Pseudo Bounding-Box Labels
Mingfei Gao, Chen Xing, Juan Carlos Niebles +4
Despite great progress in object detection, most existing methods work only on a limited set of object categories, due to the tremendous human effort needed for bounding-box annota…
DialFact: A Benchmark for Fact-Checking in Dialogue
Prakhar Gupta, Chien-Sheng Wu, Wenhao Liu +1
Fact-checking is an essential tool to mitigate the spread of misinformation and disinformation. We introduce the task of fact-checking in dialogue, which is a relatively unexplored…
MixQG: Neural Question Generation with Mixed Answer Types
Lidiya Murakhovs'ka, Chien-Sheng Wu, Philippe Laban +3
Asking good questions is an essential ability for both human and machine intelligence. However, existing neural question generation approaches mainly focus on the short factoid typ…
CaPE: Contrastive Parameter Ensembling for Reducing Hallucination in Abstractive Summarization
Prafulla Kumar Choubey, Alexander R. Fabbri, Jesse Vig +3
Hallucination is a known issue for neural abstractive summarization models. Recent work suggests that the degree of hallucination may depend on errors in the training data. In this…
Improving Gender Fairness of Pre-Trained Language Models without Catastrophic Forgetting
Zahra Fatemi, Chen Xing, Wenhao Liu +1
Existing studies addressing gender bias of pre-trained language models, usually build a small gender-neutral data set and conduct a second phase pre-training on the model with such…
HydraSum: Disentangling Stylistic Features in Text Summarization using Multi-Decoder Models
Tanya Goyal, Nazneen Fatema Rajani, Wenhao Liu +1
Summarization systems make numerous "decisions" about summary properties during inference, e.g. degree of copying, specificity and length of outputs, etc. However, these are implic…
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…
QAConv: Question Answering on Informative Conversations
Chien-Sheng Wu, Andrea Madotto, Wenhao Liu +2
This paper introduces QAConv, a new question answering (QA) dataset that uses conversations as a knowledge source. We focus on informative conversations, including business emails,…
Learning from Mistakes: Using Mis-predictions as Harm Alerts in Language Pre-Training
Chen Xing, Wenhao Liu, Caiming Xiong
Fitting complex patterns in the training data, such as reasoning and commonsense, is a key challenge for language pre-training. According to recent studies and our empirical observ…
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…