12 citations · 36 across the 8 of their papers we have counts for
8 papers
Unsupervised Contrast-Consistent Ranking with Language Models
Niklas Stoehr, Pengxiang Cheng, Jing Wang +2
Language models contain ranking-based knowledge and are powerful solvers of in-context ranking tasks. For instance, they may have parametric knowledge about the ordering of countri…
Overcoming Catastrophic Forgetting in Massively Multilingual Continual Learning
Genta Indra Winata, Lingjue Xie, Karthik Radhakrishnan +5
Real-life multilingual systems should be able to efficiently incorporate new languages as data distributions fed to the system evolve and shift over time. To do this, systems need…
Dataless Knowledge Fusion by Merging Weights of Language Models
Xisen Jin, Xiang Ren, Daniel Preotiuc-Pietro +1
Fine-tuning pre-trained language models has become the prevalent paradigm for building downstream NLP models. Oftentimes fine-tuned models are readily available but their training…
Attending to Entities for Better Text Understanding
Pengxiang Cheng, Katrin Erk
Recent progress in NLP witnessed the development of large-scale pre-trained language models (GPT, BERT, XLNet, etc.) based on Transformer (Vaswani et al. 2017), and in a range of e…
Implicit Argument Prediction as Reading Comprehension
Pengxiang Cheng, Katrin Erk
Implicit arguments, which cannot be detected solely through syntactic cues, make it harder to extract predicate-argument tuples. We present a new model for implicit argument predic…
Implicit Argument Prediction with Event Knowledge
Pengxiang Cheng, Katrin Erk
Implicit arguments are not syntactically connected to their predicates, and are therefore hard to extract. Previous work has used models with large numbers of features, evaluated o…
Crowdsourcing Information Extraction for Biomedical Systematic Reviews
Yalin Sun, Pengxiang Cheng, Shengwei Wang +4
Information extraction is a critical step in the practice of conducting biomedical systematic literature reviews. Extracted structured data can be aggregated via methods such as st…
Representing Meaning with a Combination of Logical and Distributional Models
I. Beltagy, Stephen Roller, Pengxiang Cheng +2
NLP tasks differ in the semantic information they require, and at this time no single se- mantic representation fulfills all requirements. Logic-based representations characterize…