6 citations · 19 across the 14 of their papers we have counts for
15 papers · 1 filter
Exploration of Plan-Guided Summarization for Narrative Texts: the Case of Small Language Models
Matt Grenander, Siddharth Varia, Paula Czarnowska +3
Plan-guided summarization attempts to reduce hallucinations in small language models (SLMs) by grounding generated summaries to the source text, typically by targeting fine-grained…
A Multi-Modal Multilingual Benchmark for Document Image Classification
Yoshinari Fujinuma, Siddharth Varia, Nishant Sankaran +3
Document image classification is different from plain-text document classification and consists of classifying a document by understanding the content and structure of documents su…
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…
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…
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.…
Contrastive Training Improves Zero-Shot Classification of Semi-structured Documents
Muhammad Khalifa, Yogarshi Vyas, Shuai Wang +3
We investigate semi-structured document classification in a zero-shot setting. Classification of semi-structured documents is more challenging than that of standard unstructured do…
Efficient Classification of Long Documents Using Transformers
Hyunji Hayley Park, Yogarshi Vyas, Kashif Shah
Several methods have been proposed for classifying long textual documents using Transformers. However, there is a lack of consensus on a benchmark to enable a fair comparison among…
Quantifying Social Biases in NLP: A Generalization and Empirical Comparison of Extrinsic Fairness Metrics
Paula Czarnowska, Yogarshi Vyas, Kashif Shah
Measuring bias is key for better understanding and addressing unfairness in NLP/ML models. This is often done via fairness metrics which quantify the differences in a model's behav…
Linking Entities to Unseen Knowledge Bases with Arbitrary Schemas
Yogarshi Vyas, Miguel Ballesteros
In entity linking, mentions of named entities in raw text are disambiguated against a knowledge base (KB). This work focuses on linking to unseen KBs that do not have training data…
Severing the Edge Between Before and After: Neural Architectures for Temporal Ordering of Events
Miguel Ballesteros, Rishita Anubhai, Shuai Wang +6
In this paper, we propose a neural architecture and a set of training methods for ordering events by predicting temporal relations. Our proposed models receive a pair of events wit…
Robust Cross-lingual Hypernymy Detection using Dependency Context
Shyam Upadhyay, Yogarshi Vyas, Marine Carpuat +1
Cross-lingual Hypernymy Detection involves determining if a word in one language ("fruit") is a hypernym of a word in another language ("pomme" i.e. apple in French). The ability t…
Identifying Semantic Divergences in Parallel Text without Annotations
Yogarshi Vyas, Xing Niu, Marine Carpuat
Recognizing that even correct translations are not always semantically equivalent, we automatically detect meaning divergences in parallel sentence pairs with a deep neural model o…
Parser for Abstract Meaning Representation using Learning to Search
Sudha Rao, Yogarshi Vyas, Hal Daume +1
We develop a novel technique to parse English sentences into Abstract Meaning Representation (AMR) using SEARN, a Learning to Search approach, by modeling the concept and the relat…