42 citations · 87 across the 9 of their papers we have counts for
10 papers
Prompt-driven efficient Open-set Semi-supervised Learning
Haoran Li, Chun-Mei Feng, Tao Zhou +2
Open-set semi-supervised learning (OSSL) has attracted growing interest, which investigates a more practical scenario where out-of-distribution (OOD) samples are only contained in…
Domain Adaptive Nuclei Instance Segmentation and Classification via Category-aware Feature Alignment and Pseudo-labelling
Canran Li, Dongnan Liu, Haoran Li +4
Unsupervised domain adaptation (UDA) methods have been broadly utilized to improve the models' adaptation ability in general computer vision. However, different from the natural im…
You Don't Know My Favorite Color: Preventing Dialogue Representations from Revealing Speakers' Private Personas
Haoran Li, Yangqiu Song, Lixin Fan
Social chatbots, also known as chit-chat chatbots, evolve rapidly with large pretrained language models. Despite the huge progress, privacy concerns have arisen recently: training…
CONFIT: Toward Faithful Dialogue Summarization with Linguistically-Informed Contrastive Fine-tuning
Xiangru Tang, Arjun Nair, Borui Wang +7
Factual inconsistencies in generated summaries severely limit the practical applications of abstractive dialogue summarization. Although significant progress has been achieved by u…
ConvoSumm: Conversation Summarization Benchmark and Improved Abstractive Summarization with Argument Mining
Alexander R. Fabbri, Faiaz Rahman, Imad Rizvi +4
While online conversations can cover a vast amount of information in many different formats, abstractive text summarization has primarily focused on modeling solely news articles.…
Improving Zero and Few-Shot Abstractive Summarization with Intermediate Fine-tuning and Data Augmentation
Alexander R. Fabbri, Simeng Han, Haoyuan Li +5
Models pretrained with self-supervised objectives on large text corpora achieve state-of-the-art performance on English text summarization tasks. However, these models are typicall…
Conversational Semantic Parsing
Armen Aghajanyan, Jean Maillard, Akshat Shrivastava +8
The structured representation for semantic parsing in task-oriented assistant systems is geared towards simple understanding of one-turn queries. Due to the limitations of the repr…
MTOP: A Comprehensive Multilingual Task-Oriented Semantic Parsing Benchmark
Haoran Li, Abhinav Arora, Shuohui Chen +3
Scaling semantic parsing models for task-oriented dialog systems to new languages is often expensive and time-consuming due to the lack of available datasets. Available datasets su…
General Purpose Text Embeddings from Pre-trained Language Models for Scalable Inference
Jingfei Du, Myle Ott, Haoran Li +2
The state of the art on many NLP tasks is currently achieved by large pre-trained language models, which require a considerable amount of computation. We explore a setting where ma…
Emerging Cross-lingual Structure in Pretrained Language Models
Shijie Wu, Alexis Conneau, Haoran Li +2
We study the problem of multilingual masked language modeling, i.e. the training of a single model on concatenated text from multiple languages, and present a detailed study of sev…