Xiaoya Li

Glyce: Glyph-vectors for Chinese Character Representations

2019 · arXiv (Cornell University)

It is intuitive that NLP tasks for logographic languages like Chinese should benefit from the use of the glyph information in those languages. However, due to the lack of rich pictographic evidence in glyphs and …

A General Framework for Defending Against Backdoor Attacks via Influence Graph

2021 · arXiv (Cornell University)

In this work, we propose a new and general framework to defend against backdoor attacks, inspired by the fact that attack triggers usually follow a \textsc{specific} type of attacking pattern, and therefore, poisoned training examples …

GPT-NER: Named Entity Recognition via Large Language Models

2023 · arXiv (Cornell University)

Despite the fact that large-scale Language Models (LLM) have achieved SOTA performances on a variety of NLP tasks, its performance on NER is still significantly below supervised baselines. This is due to the gap between …

Pushing the Limits of ChatGPT on NLP Tasks

2023 · arXiv (Cornell University)

Despite the success of ChatGPT, its performances on most NLP tasks are still well below the supervised baselines. In this work, we looked into the causes, and discovered that its subpar performance was caused by …

Is Word Segmentation Necessary for Deep Learning of Chinese Representations?

2019

Segmenting a chunk of text into words is usually the first step of processing Chinese text, but its necessity has rarely been explored.

Entity-Relation Extraction as Multi-Turn Question Answering

2019

In this paper, we propose a new paradigm for the task of entity-relation extraction. We cast the task as a multi-turn question answering problem, i.e., the extraction of entities and relations is transformed to the …

Dice Loss for Data-imbalanced NLP Tasks

2020

Many NLP tasks such as tagging and machine reading comprehension (MRC) are faced with the severe data imbalance issue: negative examples significantly outnumber positive ones, and the huge number of easy-negative examples overwhelms training. The …

A Unified MRC Framework for Named Entity Recognition

2020

The task of named entity recognition (NER) is normally divided into nested NER and flat NER depending on whether named entities are nested or not. Models are usually separately developed for the two tasks, since …