activity
20162024
most citedDyNet: The Dynamic Neural Network Toolkit

343 citations · 363 across the 9 of their papers we have counts for

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Showing cs.CLShow all

8 papers · 1 filter

cs.CL20243 cited

General Purpose Verification for Chain of Thought Prompting

Robert Vacareanu, Anurag Pratik, Evangelia Spiliopoulou +6

Many of the recent capabilities demonstrated by Large Language Models (LLMs) arise primarily from their ability to exploit contextual information. In this paper, we explore ways to…

cs.CL2023

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…

cs.CL20232 cited

Taxonomy Expansion for Named Entity Recognition

Karthikeyan K, Yogarshi Vyas, Jie Ma +7

Training a Named Entity Recognition (NER) model often involves fixing a taxonomy of entity types. However, requirements evolve and we might need the NER model to recognize addition…

cs.CL2023

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…

cs.CL20232 cited

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…

cs.CL20231 cited

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…