← Search

Owen Rambow

18 accepted papers

2025

Active Few-Shot Learning for Text Classification

NAACL 2025long

The rise of Large Language Models (LLMs) has boosted the use of Few-Shot Learning (FSL) methods in natural language processing, achieving acceptable performance even when working with limited training data. The goal of FSL is to effectively utilize a small number of annotated samples in the learning…

2025

LLMs can Perform Multi-Dimensional Analytic Writing Assessments: A Case Study of L2 Graduate-Level Academic English Writing

ACL 2025long

The paper explores the performance of LLMs in the context of multi-dimensional analytic writing assessments, i.e. their ability to provide both scores and comments based on multiple assessment criteria. Using a corpus of literature reviews written by L2 graduate students and assessed by human expert…

2025

LVLMs are Bad at Overhearing Human Referential Communication

EMNLP 2025

During spontaneous conversations, speakers collaborate on novel referring expressions, which they can then re-use in subsequent conversations. Understanding such referring expressions is an important ability for an embodied agent, so that it can carry out tasks in the real world. This requires integ

Cited by 0SourcePDFScholar
2025

Residualized Similarity for Faithfully Explainable Authorship Verification

EMNLP 2025

Responsible use of Authorship Verification (AV) systems not only requires high accuracy but also interpretable solutions. More importantly, for systems to be used to make decisions with real-world consequences requires the model’s prediction to be explainable using interpretable features that can be

2024

Opinion Mining Using Pre-Trained Large Language Models: Identifying the Type, Polarity, Intensity, Expression, and Source of Private States

COLING 2024main

Opinion mining is an important task in natural language processing. The MPQA Opinion Corpus is a fine-grained and comprehensive dataset of private states (i.e., the condition of a source who has an attitude which may be directed toward a target) based on context. Although this dataset was released y…

2024

Views Are My Own, but Also Yours: Benchmarking Theory of Mind Using Common Ground

ACL 2024findings

Evaluating the theory of mind (ToM) capabilities of language models (LMs) has recently received a great deal of attention. However, many existing benchmarks rely on synthetic data, which risks misaligning the resulting experiments with human behavior. We introduce the first ToM dataset based on natu…

2023

A Cautious Generalization Goes a Long Way: Learning Morphophonological Rules

ACL 2023long

Explicit linguistic knowledge, encoded by resources such as rule-based morphological analyzers, continues to prove useful in downstream NLP tasks, especially for low-resource languages and dialects. Rules are an important asset in descriptive linguistic grammars. However, creating such resources is…

Cited by 5SourcePDFScholar
2023

Deep Active Learning for Morphophonological Processing

ACL 2023short

Building a system for morphological processing is a challenging task in morphologically complex languages like Arabic. Although there are some deep learning based models that achieve successful results, these models rely on a large amount of annotated data. Building such datasets, specially for some…

Cited by 1SourcePDFScholar
2023

Finding Common Ground: Annotating and Predicting Common Ground in Spoken Conversations

EMNLP 2023long findings

When we communicate with other humans, we do not simply generate a sequence of words. Rather, we use our cognitive state (beliefs, desires, intentions) and our model of the audience’s cognitive state to create utterances that affect the audience’s cognitive state in the intended manner. An important…

Cited by 0SourcecodeScholar
2023

NORMSAGE: Multi-Lingual Multi-Cultural Norm Discovery from Conversations On-the-Fly

EMNLP 2023long main

Knowledge of norms is needed to understand and reason about acceptable behavior in human communication and interactions across sociocultural scenarios. Most computational research on norms has focused on a single culture, and manually built datasets, from non-conversational settings. We address thes…

Cited by 0SourcecodeScholar
2022

From Stance to Concern: Adaptation of Propositional Analysis to New Tasks and Domains

ACL 2022findings

We present a generalized paradigm for adaptation of propositional analysis (predicate-argument pairs) to new tasks and domains. We leverage an analogy between stances (belief-driven sentiment) and concerns (topical issues with moral dimensions/endorsements) to produce an explanatory representation.…

2022

Re-Examining FactBank: Predicting the Author’s Presentation of Factuality

COLING 2022main

We present a corrected version of a subset of the FactBank data set. Previously published results on FactBank are no longer valid. We perform experiments on FactBank using multiple training paradigms, data smoothing techniques, and polarity classifiers. We argue that f-measure is an important altern…

Cited by 10SourcePDFScholar