526 citations · 1.1k across the 32 of their papers we have counts for
5 papers · 1 filter
Characterizing and Measuring Linguistic Dataset Drift
Tyler A. Chang, Kishaloy Halder, Neha Anna John +4
NLP models often degrade in performance when real world data distributions differ markedly from training data. However, existing dataset drift metrics in NLP have generally not con…
A Weak Supervision Approach for Few-Shot Aspect Based Sentiment
Robert Vacareanu, Siddharth Varia, Kishaloy Halder +5
We explore how weak supervision on abundant unlabeled data can be leveraged to improve few-shot performance in aspect-based sentiment analysis (ABSA) tasks. We propose a pipeline a…
Comparing Biases and the Impact of Multilingual Training across Multiple Languages
Sharon Levy, Neha Anna John, Ling Liu +6
Studies in bias and fairness in natural language processing have primarily examined social biases within a single language and/or across few attributes (e.g. gender, race). However…
Simple Yet Effective Synthetic Dataset Construction for Unsupervised Opinion Summarization
Ming Shen, Jie Ma, Shuai Wang +4
Opinion summarization provides an important solution for summarizing opinions expressed among a large number of reviews. However, generating aspect-specific and general summaries i…
Dynamic Benchmarking of Masked Language Models on Temporal Concept Drift with Multiple Views
Katerina Margatina, Shuai Wang, Yogarshi Vyas +3
Temporal concept drift refers to the problem of data changing over time. In NLP, that would entail that language (e.g. new expressions, meaning shifts) and factual knowledge (e.g.…