62 citations · 430 across the 52 of their papers we have counts for
20 papers · 1 filter
Human-in-the-loop Abstractive Dialogue Summarization
Jiaao Chen, Mohan Dodda, Diyi Yang
Abstractive dialogue summarization has received increasing attention recently. Despite the fact that most of the current dialogue summarization systems are trained to maximize the…
On Second Thought, Let's Not Think Step by Step! Bias and Toxicity in Zero-Shot Reasoning
Omar Shaikh, Hongxin Zhang, William Held +2
Generating a Chain of Thought (CoT) has been shown to consistently improve large language model (LLM) performance on a wide range of NLP tasks. However, prior work has mainly focus…
DAMP: Doubly Aligned Multilingual Parser for Task-Oriented Dialogue
William Held, Christopher Hidey, Fei Liu +4
Modern virtual assistants use internal semantic parsing engines to convert user utterances to actionable commands. However, prior work has demonstrated that semantic parsing is a d…
Multi-VALUE: A Framework for Cross-Dialectal English NLP
Caleb Ziems, William Held, Jingfeng Yang +3
Dialect differences caused by regional, social, and economic factors cause performance discrepancies for many groups of language technology users. Inclusive and equitable language…
ConStruct-VL: Data-Free Continual Structured VL Concepts Learning
James Seale Smith, Paola Cascante-Bonilla, Assaf Arbelle +7
Recently, large-scale pre-trained Vision-and-Language (VL) foundation models have demonstrated remarkable capabilities in many zero-shot downstream tasks, achieving competitive res…
WHEN FLUE MEETS FLANG: Benchmarks and Large Pre-trained Language Model for Financial Domain
Raj Sanjay Shah, Kunal Chawla, Dheeraj Eidnani +7
Pre-trained language models have shown impressive performance on a variety of tasks and domains. Previous research on financial language models usually employs a generic training s…
Geographic Citation Gaps in NLP Research
Mukund Rungta, Janvijay Singh, Saif M. Mohammad +1
In a fair world, people have equitable opportunities to education, to conduct scientific research, to publish, and to get credit for their work, regardless of where they live. Howe…
Robustness of Demonstration-based Learning Under Limited Data Scenario
Hongxin Zhang, Yanzhe Zhang, Ruiyi Zhang +1
Demonstration-based learning has shown great potential in stimulating pretrained language models' ability under limited data scenario. Simply augmenting the input with some demonst…
Shapley Head Pruning: Identifying and Removing Interference in Multilingual Transformers
William Held, Diyi Yang
Multilingual transformer-based models demonstrate remarkable zero and few-shot transfer across languages by learning and reusing language-agnostic features. However, as a fixed-siz…
DoubleMix: Simple Interpolation-Based Data Augmentation for Text Classification
Hui Chen, Wei Han, Diyi Yang +1
This paper proposes a simple yet effective interpolation-based data augmentation approach termed DoubleMix, to improve the robustness of models in text classification. DoubleMix fi…
A Sketch Is Worth a Thousand Words: Image Retrieval with Text and Sketch
Patsorn Sangkloy, Wittawat Jitkrittum, Diyi Yang +1
We address the problem of retrieving images with both a sketch and a text query. We present TASK-former (Text And SKetch transformer), an end-to-end trainable model for image retri…
SeqZero: Few-shot Compositional Semantic Parsing with Sequential Prompts and Zero-shot Models
Jingfeng Yang, Haoming Jiang, Qingyu Yin +3
Recent research showed promising results on combining pretrained language models (LMs) with canonical utterance for few-shot semantic parsing. The canonical utterance is often leng…
SUBS: Subtree Substitution for Compositional Semantic Parsing
Jingfeng Yang, Le Zhang, Diyi Yang
Although sequence-to-sequence models often achieve good performance in semantic parsing for i.i.d. data, their performance is still inferior in compositional generalization. Severa…
TreeMix: Compositional Constituency-based Data Augmentation for Natural Language Understanding
Le Zhang, Zichao Yang, Diyi Yang
Data augmentation is an effective approach to tackle over-fitting. Many previous works have proposed different data augmentations strategies for NLP, such as noise injection, word…
The Moral Integrity Corpus: A Benchmark for Ethical Dialogue Systems
Caleb Ziems, Jane A. Yu, Yi-Chia Wang +2
Conversational agents have come increasingly closer to human competence in open-domain dialogue settings; however, such models can reflect insensitive, hurtful, or entirely incoher…
Inducing Positive Perspectives with Text Reframing
Caleb Ziems, Minzhi Li, Anthony Zhang +1
Sentiment transfer is one popular example of a text style transfer task, where the goal is to reverse the sentiment polarity of a text. With a sentiment reversal comes also a rever…
Continual Sequence Generation with Adaptive Compositional Modules
Yanzhe Zhang, Xuezhi Wang, Diyi Yang
Continual learning is essential for real-world deployment when there is a need to quickly adapt the model to new tasks without forgetting knowledge of old tasks. Existing work on c…
VALUE: Understanding Dialect Disparity in NLU
Caleb Ziems, Jiaao Chen, Camille Harris +2
English Natural Language Understanding (NLU) systems have achieved great performances and even outperformed humans on benchmarks like GLUE and SuperGLUE. However, these benchmarks…
Fantastic Questions and Where to Find Them: FairytaleQA -- An Authentic Dataset for Narrative Comprehension
Ying Xu, Dakuo Wang, Mo Yu +15
Question answering (QA) is a fundamental means to facilitate assessment and training of narrative comprehension skills for both machines and young children, yet there is scarcity o…
Leveraging Expert Guided Adversarial Augmentation For Improving Generalization in Named Entity Recognition
Aaron Reich, Jiaao Chen, Aastha Agrawal +2
Named Entity Recognition (NER) systems often demonstrate great performance on in-distribution data, but perform poorly on examples drawn from a shifted distribution. One way to eva…