Fred Morstatter


2024

pdf bib
Don’t Blame the Data, Blame the Model: Understanding Noise and Bias When Learning from Subjective Annotations
Abhishek Anand | Negar Mokhberian | Prathyusha Kumar | Anweasha Saha | Zihao He | Ashwin Rao | Fred Morstatter | Kristina Lerman
Proceedings of the 1st Workshop on Uncertainty-Aware NLP (UncertaiNLP 2024)

Researchers have raised awareness about the harms of aggregating labels especially in subjective tasks that naturally contain disagreements among human annotators. In this work we show that models that are only provided aggregated labels show low confidence on high-disagreement data instances. While previous studies consider such instances as mislabeled, we argue that the reason the high-disagreement text instances have been hard-to-learn is that the conventional aggregated models underperform in extracting useful signals from subjective tasks. Inspired by recent studies demonstrating the effectiveness of learning from raw annotations, we investigate classifying using Multiple Ground Truth (Multi-GT) approaches. Our experiments show an improvement of confidence for the high-disagreement instances.

pdf bib
“Define Your Terms” : Enhancing Efficient Offensive Speech Classification with Definition
Huy Nghiem | Umang Gupta | Fred Morstatter
Proceedings of the 18th Conference of the European Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

The propagation of offensive content through social media channels has garnered attention of the research community. Multiple works have proposed various semantically related yet subtle distinct categories of offensive speech. In this work, we explore meta-learning approaches to leverage the diversity of offensive speech corpora to enhance their reliable and efficient detection. We propose a joint embedding architecture that incorporates the input’s label and definition for classification via Prototypical Network. Our model achieves at least 75% of the maximal F1-score while using less than 10% of the available training data across 4 datasets. Our experimental findings also provide a case study of training strategies valuable to combat resource scarcity.

2023

pdf bib
Modeling Cross-Cultural Pragmatic Inference with Codenames Duet
Omar Shaikh | Caleb Ziems | William Held | Aryan Pariani | Fred Morstatter | Diyi Yang
Findings of the Association for Computational Linguistics: ACL 2023

Pragmatic reference enables efficient interpersonal communication. Prior work uses simple reference games to test models of pragmatic reasoning, often with unidentified speakers and listeners. In practice, however, speakers’ sociocultural background shapes their pragmatic assumptions. For example, readers of this paper assume NLP refers to Natural Language Processing, and not “Neuro-linguistic Programming.” This work introduces the Cultural Codes dataset, which operationalizes sociocultural pragmatic inference in a simple word reference game. Cultural Codes is based on the multi-turn collaborative two-player game, Codenames Duet. Our dataset consists of 794 games with 7,703 turns, distributed across 153 unique players. Alongside gameplay, we collect information about players’ personalities, values, and demographics. Utilizing theories of communication and pragmatics, we predict each player’s actions via joint modeling of their sociocultural priors and the game context. Our experiments show that accounting for background characteristics significantly improves model performance for tasks related to both clue-giving and guessing, indicating that sociocultural priors play a vital role in gameplay decisions.

pdf bib
Temporal Knowledge Graph Forecasting Without Knowledge Using In-Context Learning
Dong-Ho Lee | Kian Ahrabian | Woojeong Jin | Fred Morstatter | Jay Pujara
Proceedings of the 2023 Conference on Empirical Methods in Natural Language Processing

Temporal knowledge graph (TKG) forecasting benchmarks challenge models to predict future facts using knowledge of past facts. In this paper, we develop an approach to use in-context learning (ICL) with large language models (LLMs) for TKG forecasting. Our extensive evaluation compares diverse baselines, including both simple heuristics and state-of-the-art (SOTA) supervised models, against pre-trained LLMs across several popular benchmarks and experimental settings. We observe that naive LLMs perform on par with SOTA models, which employ carefully designed architectures and supervised training for the forecasting task, falling within the (-3.6%, +1.5%) Hits@1 margin relative to the median performance. To better understand the strengths of LLMs for forecasting, we explore different approaches for selecting historical facts, constructing prompts, controlling information propagation, and parsing outputs into a probability distribution. A surprising finding from our experiments is that LLM performance endures (±0.4% Hit@1) even when semantic information is removed by mapping entities/relations to arbitrary numbers, suggesting that prior semantic knowledge is unnecessary; rather, LLMs can leverage the symbolic patterns in the context to achieve such a strong performance. Our analysis also reveals that ICL enables LLMs to learn irregular patterns from the historical context, going beyond frequency and recency biases

pdf bib
AutoTriggER: Label-Efficient and Robust Named Entity Recognition with Auxiliary Trigger Extraction
Dong-Ho Lee | Ravi Kiran Selvam | Sheikh Muhammad Sarwar | Bill Yuchen Lin | Fred Morstatter | Jay Pujara | Elizabeth Boschee | James Allan | Xiang Ren
Proceedings of the 17th Conference of the European Chapter of the Association for Computational Linguistics

Deep neural models for named entity recognition (NER) have shown impressive results in overcoming label scarcity and generalizing to unseen entities by leveraging distant supervision and auxiliary information such as explanations. However, the costs of acquiring such additional information are generally prohibitive. In this paper, we present a novel two-stage framework (AutoTriggER) to improve NER performance by automatically generating and leveraging “entity triggers” which are human-readable cues in the text that help guide the model to make better decisions. Our framework leverages post-hoc explanation to generate rationales and strengthens a model’s prior knowledge using an embedding interpolation technique. This approach allows models to exploit triggers to infer entity boundaries and types instead of solely memorizing the entity words themselves. Through experiments on three well-studied NER datasets, AutoTriggER shows strong label-efficiency, is capable of generalizing to unseen entities, and outperforms the RoBERTa-CRF baseline by nearly 0.5 F1 points on average.

pdf bib
Pipeline for modeling causal beliefs from natural language
John Priniski | Ishaan Verma | Fred Morstatter
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 3: System Demonstrations)

We present a causal language analysis pipeline that leverages a Large Language Model to identify causal claims made in natural language documents, and aggregates claims across a corpus to produce a causal claim network. The pipeline then applies a clustering algorithm that groups causal claims based on their semantic topics. We demonstrate the pipeline by modeling causal belief systems surrounding the Covid-19 vaccine from tweets.

2022

pdf bib
Attributing Fair Decisions with Attention Interventions
Ninareh Mehrabi | Umang Gupta | Fred Morstatter | Greg Ver Steeg | Aram Galstyan
Proceedings of the 2nd Workshop on Trustworthy Natural Language Processing (TrustNLP 2022)

The widespread use of Artificial Intelligence (AI) in consequential domains, such as health-care and parole decision-making systems, has drawn intense scrutiny on the fairness of these methods. However, ensuring fairness is often insufficient as the rationale for a contentious decision needs to be audited, understood, and defended. We propose that the attention mechanism can be used to ensure fair outcomes while simultaneously providing feature attributions to account for how a decision was made. Toward this goal, we design an attention-based model that can be leveraged as an attribution framework. It can identify features responsible for both performance and fairness of the model through attention interventions and attention weight manipulation. Using this attribution framework, we then design a post-processing bias mitigation strategy and compare it with a suite of baselines. We demonstrate the versatility of our approach by conducting experiments on two distinct data types, tabular and textual.

pdf bib
Robust Conversational Agents against Imperceptible Toxicity Triggers
Ninareh Mehrabi | Ahmad Beirami | Fred Morstatter | Aram Galstyan
Proceedings of the 2022 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

Warning: this paper contains content that maybe offensive or upsetting. Recent research in Natural Language Processing (NLP) has advanced the development of various toxicity detection models with the intention of identifying and mitigating toxic language from existing systems. Despite the abundance of research in this area, less attention has been given to adversarial attacks that force the system to generate toxic language and the defense against them. Existing work to generate such attacks is either based on human-generated attacks which is costly and not scalable or, in case of automatic attacks, the attack vector does not conform to human-like language, which can be detected using a language model loss. In this work, we propose attacks against conversational agents that are imperceptible, i.e., they fit the conversation in terms of coherency, relevancy, and fluency, while they are effective and scalable, i.e., they can automatically trigger the system into generating toxic language. We then propose a defense mechanism against such attacks which not only mitigates the attack but also attempts to maintain the conversational flow. Through automatic and human evaluations, we show that our defense is effective at avoiding toxic language generation even against imperceptible toxicity triggers while the generated language fits the conversation in terms of coherency and relevancy. Lastly, we establish the generalizability of such a defense mechanism on language generation models beyond conversational agents.

2021

pdf bib
Lawyers are Dishonest? Quantifying Representational Harms in Commonsense Knowledge Resources
Ninareh Mehrabi | Pei Zhou | Fred Morstatter | Jay Pujara | Xiang Ren | Aram Galstyan
Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing

Warning: this paper contains content that may be offensive or upsetting. Commonsense knowledge bases (CSKB) are increasingly used for various natural language processing tasks. Since CSKBs are mostly human-generated and may reflect societal biases, it is important to ensure that such biases are not conflated with the notion of commonsense. Here we focus on two widely used CSKBs, ConceptNet and GenericsKB, and establish the presence of bias in the form of two types of representational harms, overgeneralization of polarized perceptions and representation disparity across different demographic groups in both CSKBs. Next, we find similar representational harms for downstream models that use ConceptNet. Finally, we propose a filtering-based approach for mitigating such harms, and observe that our filtered-based approach can reduce the issues in both resources and models but leads to a performance drop, leaving room for future work to build fairer and stronger commonsense models.

pdf bib
ForecastQA: A Question Answering Challenge for Event Forecasting with Temporal Text Data
Woojeong Jin | Rahul Khanna | Suji Kim | Dong-Ho Lee | Fred Morstatter | Aram Galstyan | Xiang Ren
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Event forecasting is a challenging, yet important task, as humans seek to constantly plan for the future. Existing automated forecasting studies rely mostly on structured data, such as time-series or event-based knowledge graphs, to help predict future events. In this work, we aim to formulate a task, construct a dataset, and provide benchmarks for developing methods for event forecasting with large volumes of unstructured text data. To simulate the forecasting scenario on temporal news documents, we formulate the problem as a restricted-domain, multiple-choice, question-answering (QA) task. Unlike existing QA tasks, our task limits accessible information, and thus a model has to make a forecasting judgement. To showcase the usefulness of this task formulation, we introduce ForecastQA, a question-answering dataset consisting of 10,392 event forecasting questions, which have been collected and verified via crowdsourcing efforts. We present our experiments on ForecastQA using BERTbased models and find that our best model achieves 61.0% accuracy on the dataset, which still lags behind human performance by about 19%. We hope ForecastQA will support future research efforts in bridging this gap.

pdf bib
Identifying Distributional Perspectives from Colingual Groups
Yufei Tian | Tuhin Chakrabarty | Fred Morstatter | Nanyun Peng
Proceedings of the Ninth International Workshop on Natural Language Processing for Social Media

Discrepancies exist among different cultures or languages. A lack of mutual understanding among different colingual groups about the perspectives on specific values or events may lead to uninformed decisions or biased opinions. Thus, automatically understanding the group perspectives can provide essential back-ground for many natural language processing tasks. In this paper, we study colingual groups and use language corpora as a proxy to identify their distributional perspectives. We present a novel computational approach to learn shared understandings, and benchmark our method by building culturally-aware models for the English, Chinese, and Japanese languages. Ona held out set of diverse topics, including marriage, corruption, democracy, etc., our model achieves high correlation with human judgements regarding intra-group values and inter-group differences

2016

pdf bib
A Novel Measure for Coherence in Statistical Topic Models
Fred Morstatter | Huan Liu
Proceedings of the 54th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

2014

pdf bib
Finding Eyewitness Tweets During Crises
Fred Morstatter | Nichola Lubold | Heather Pon-Barry | Jürgen Pfeffer | Huan Liu
Proceedings of the ACL 2014 Workshop on Language Technologies and Computational Social Science