Papers by Claire Cardie
Multi-Source Cross-Lingual Model Transfer: Learning What to Share (P19-1)
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| Challenge: | Cross-lingual transfer learning (CLTL) is a viable method for building NLP models for a low-resource target language . however, many languages lack the labeled training data necessary for training deep neural nets for varying NLP tasks. |
| Approach: | They propose a cross-lingual transfer learning method that leverages annotated data from other languages to build NLP models for a target language. |
| Outcome: | The proposed model achieves significant performance gains over prior art over multiple text classification and sequence tagging tasks including a large-scale industry dataset. |
Multinomial Adversarial Networks for Multi-Domain Text Classification (N18-1)
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| Challenge: | Existing methods for text classification are domain-dependent, but there is no annotated data for some domains. |
| Approach: | They propose a multinomial adversarial network to tackle multi-domain text classification . they show that MANs significantly outperform prior art on the MDTC task . |
| Outcome: | The proposed model outperforms the prior art on the multi-domain text classification task. |
Adapting Fake News Detection to the Era of Large Language Models (2024.findings-naacl)
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| Challenge: | a gap exists in understanding the interplay between machine-paraphrased real news, machine-generated fake news, and human-written real news . false information is easier to generate but harder to detect due to the bias of detectors against machine-generated texts . |
| Approach: | They propose a strategy to adapt fake news detectors to the era of large language models and AI-driven content creation . |
| Outcome: | The proposed detectors perform well on human-written articles but not vice versa . the proposed detector should be trained on datasets with lower machine-generated news ratio than the test set . |
Faithful or Extractive? On Mitigating the Faithfulness-Abstractiveness Trade-off in Abstractive Summarization (2022.acl-long)
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| Challenge: | Abstractive summarization systems still suffer from faithfulness errors, authors say . prior work has proposed models that improve faithfulness, but it is unclear whether this improvement comes from an increased level of extractiveness of the outputs. |
| Approach: | They propose a faithfulness-abstractiveness trade-off curve that serves as a control . they also learn a selector to identify the most faithful and abstractive summary for a given document . |
| Outcome: | The proposed model achieves higher faithfulness scores while being abstractive than the baseline system on two datasets. |
A Corpus for Modeling User and Language Effects in Argumentation on Online Debating (P19-1)
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| Challenge: | Existing argumentation datasets have allowed only limited assessment of "user" traits because information on background of users is generally unavailable. |
| Approach: | They present a dataset of 78,376 debates generated over a 10-year period along with surprisingly comprehensive participant profiles. |
| Outcome: | The proposed dataset includes 78,376 debates generated over a 10-year period along with comprehensive participant profiles. |
Hop, Union, Generate: Explainable Multi-hop Reasoning without Rationale Supervision (2023.emnlp-main)
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| Challenge: | Existing methods rely on supervision for both answers and rationales, but they have limited capacities in modeling interactions between sentences, let alone reasoning across multiple documents. |
| Approach: | They propose a principled, probabilistic approach for training explainable multi-hop question answering systems without rationale supervision. |
| Outcome: | The proposed method is more accurate at selecting rationales than previous methods while maintaining similar accuracy in predicting answers. |
Exploring the Role of Argument Structure in Online Debate Persuasion (2020.emnlp-main)
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| Challenge: | Existing work in NLP has shown that linguistic features extracted from debate text and features encoding the characteristics of the audience are both critical in persuasion studies. |
| Approach: | They propose to incorporate argument structure features into an LSTM-based model to assess the persuasiveness of debates. |
| Outcome: | The proposed model incorporates argument structure features to predict debaters that make the most convincing arguments on online debate forums. |
Adding Chit-Chat to Enhance Task-Oriented Dialogues (2021.naacl-main)
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Kai Sun, Seungwhan Moon, Paul Crook, Stephen Roller, Becka Silvert, Bing Liu, Zhiguang Wang, Honglei Liu, Eunjoon Cho, Claire Cardie
| Challenge: | Existing dialogue systems focus on functional goals, open-domain chatbots on socially engaging conversations. |
| Approach: | They propose to add chit-chat to ENhance Task-ORiented dialogues by a human-assisted data collection approach to augment task-oriented dialogues with minimal annotation effort. |
| Outcome: | The proposed models can code-switch between task and chit-chat to be more engaging, interesting, knowledgeable, and humanlike while maintaining competitive task performance. |
Better LLM Reasoning via Dual-Play (2026.findings-acl)
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| Challenge: | Large Language Models (LLMs) have made remarkable progress through Reinforcement Learning with Verifiable Rewards (RLVR) however, external supervision remains a bottleneck for tasks and domains for which supervised data are scarce or non-existent. |
| Approach: | They propose a novel dual-play framework that adversarially trains two models initialized from the same base model. |
| Outcome: | The proposed framework improves the math reasoning performance of large language models. |
Document-Level Event Role Filler Extraction using Multi-Granularity Contextualized Encoding (2020.acl-main)
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| Challenge: | Document-level event extraction requires a view of a larger context to determine which spans of text correspond to event role fillers. |
| Approach: | They propose a multi-granularity reader to dynamically aggregate information captured by neural representations learned at different levels of granularities. |
| Outcome: | The proposed model performs substantially better than previous models on the MUC-4 event extraction dataset. |
Unsupervised Multilingual Word Embeddings (D18-1)
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| Challenge: | Prior art for learning UMWEs relies on a number of independently trained UBWEs to obtain multilingual embeddings. |
| Approach: | They propose a fully unsupervised framework that exploits the relations between all language pairs to learn multilingual embeddings without cross-lingual supervision. |
| Outcome: | The proposed framework outperforms supervised approaches on multilingual word translation and cross-lingual word similarity and beats a number of other approaches trained with cross-linguistic resources. |
Exploring the Role of Prior Beliefs for Argument Persuasion (N18-1)
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| Challenge: | Recent studies in natural language processing (NLP) have shown that the language of opinion holders and their patterns of interaction play a key role in changing the mind of a reader. |
| Approach: | They propose to use a dataset to study the effect of language use vs. prior beliefs on persuasion in a controlled setting that takes into account political and religious ideology. |
| Outcome: | The proposed controlled setting takes into account political and religious ideology and shows that prior beliefs play a more important role than language use effects. |
Intrinsic Evaluation of Summarization Datasets (2020.emnlp-main)
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| Challenge: | Almost all popular summarization datasets do not come with inherent quality assurance guarantees. |
| Approach: | They propose to use 5 metrics to evaluate quality of summarization datasets . they find that data usage in recent summarizing research is inconsistent with the properties of the data. |
| Outcome: | The proposed metrics can be inexpensive heuristics for detecting generically low quality examples. |
Automatic Error Analysis for Document-level Information Extraction (2022.acl-long)
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| Challenge: | Document-level information extraction (IE) tasks have been revisited in earnest . evaluation of the approaches has been limited in a number of dimensions . |
| Approach: | They propose a transformation-based framework for automating error analysis in document-level event and (N-ary) relation extraction. |
| Outcome: | The proposed framework compares two state-of-the-art document-level template-filling approaches on datasets from three domains and four systems from the MUC-4 evaluation. |
Dialogue-Based Relation Extraction (2020.acl-main)
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| Challenge: | Existing dialogue-based relation extraction tasks focus on texts from formal genres such as professionally written and edited news reports or well-edited websites. |
| Approach: | They propose to use DialogRE to study cross-sentence relation extraction . they propose to annotate 36 possible relation types between arguments in dialogues . |
| Outcome: | The proposed dataset supports the prediction of relation(s) between two arguments that appear in a dialogue. |
Event Extraction by Answering (Almost) Natural Questions (2020.emnlp-main)
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| Challenge: | Existing work in event argument extraction relies heavily on entity recognition as a preprocessing/concurrent step, causing error propagation. |
| Approach: | They propose a question answering task that extracts event arguments in an end-to-end manner. |
| Outcome: | The proposed framework outperforms prior work on the ACE 2005 task on event argument extraction. |
I Could’ve Asked That: Reformulating Unanswerable Questions (2024.emnlp-main)
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| Challenge: | Existing large language models do not assist users in reformulating unanswerable questions . a recent study found that the models failed to reformulate questions based on assumptions that conflict with or cannot be verified with the information available in documents. |
| Approach: | They evaluate open-source and proprietary LLMs on couldAsk to evaluate their performance . they found that GPT-4 and Llama2-7B successfully reformulate questions only 26% and 12% of the time . |
| Outcome: | The proposed model successfully reformulates questions only 26% and 12% of the time . the proposed model is not able to reformulate questions, but it can be improved . |
Leveraging Structured Metadata for Improving Question Answering on the Web (2020.aacl-main)
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| Challenge: | Using metadata information from web pages can improve the performance of answer passage selection/reranking models. |
| Approach: | They propose a neural passage selection model that leverages metadata information with a fine-grained encoding strategy to learn the representation for metadata predicates in a hierarchical way. |
| Outcome: | The proposed model outperforms baseline models on the MS MARCO and Recipe-MARCO datasets and shows that it is more accurate than baseline models. |
WildVis: Open Source Visualizer for Million-Scale Chat Logs in the Wild (2024.emnlp-demo)
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| Challenge: | Currently, the volume and complexity of chat logs makes it difficult to analyze individual conversations. |
| Approach: | They propose a tool that enables fast, versatile, and large-scale conversation analysis by combining search and visualization capabilities with a list of criteria. |
| Outcome: | The proposed tool can be extended to handle millions of chat logs and other datasets. |
Compositional Task-Oriented Parsing as Abstractive Question Answering (2022.naacl-main)
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| Challenge: | Task-oriented parsing (TOP) aims to convert natural language into machine-readable representations of specific tasks, such as setting an alarm. |
| Approach: | They propose to reduce TOP to abstractive question answering by using canonical paraphrasing to generate linearized parse trees. |
| Outcome: | The proposed technique outperforms state-of-the-art methods in full-data settings while achieving dramatic improvements in few-shot settings. |
BeSt: The Belief and Sentiment Corpus (2022.lrec-1)
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Jennifer Tracey, Owen Rambow, Claire Cardie, Adam Dalton, Hoa Trang Dang, Mona Diab, Bonnie Dorr, Louise Guthrie, Magdalena Markowska, Smaranda Muresan, Vinodkumar Prabhakaran, Samira Shaikh, Tomek Strzalkowski
| Challenge: | a corpus of propositional content is a set of cognitive attitudes of different agents towards a text . propositional attitudes are a cognitive attitude, including belief and sentiment, towards . |
| Approach: | They propose a corpus which records cognitive state: who believes what, who has what sentiment . they use newswire and discussion forums in Chinese, English, and Spanish . |
| Outcome: | The proposed corpus records who believes what (i.e., factuality) and who has what sentiment towards what. |
Determining Relative Argument Specificity and Stance for Complex Argumentative Structures (P19-1)
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| Challenge: | Existing work on claim specificity and stance has been limited to shallow arguments . a system that can determine the stance of claims employed in argumentation is not sufficient . |
| Approach: | They propose to use a dataset of manually curated argument trees to study claim specificity and stance in argumentation. |
| Outcome: | The proposed dataset consists of manually curated argument trees for 741 controversial topics covering 95,312 unique claims. |
GRIT: Generative Role-filler Transformers for Document-level Event Entity Extraction (2021.eacl-main)
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| Challenge: | Document-level role-filler entity extraction (REE) is a classic problem in information extraction (IE) and NLP. |
| Approach: | They propose a document-level role-filler entity extraction framework that can make extraction decisions across sentence boundaries and implicitly recognizes noun phrase coreference structure. |
| Outcome: | The proposed framework performs better than previous models on a MUC-4 dataset. |
GSM-Noise: Exploring and Enhancing Large Language Models’ Reasoning under Noisy Inputs (2026.findings-acl)
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| Challenge: | Large language models struggle when dealing with complex, ill-formed, or noisy inputs . open-source models are less robust, while closed-source ones are more robust . |
| Approach: | They propose to use GSM-Noise to refine inputs before engaging in in-depth analysis to improve LLM robustness under noisy conditions. |
| Outcome: | The proposed model can achieve consistent performance gains under noisy conditions with prompt engineering, supervised finetuning, and reinforcement learning. |
Towards Dynamic Computation Graphs via Sparse Latent Structure (D18-1)
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| Challenge: | Existing approaches to learn latent structure are limited by factorization assumptions or end-to-end differentiability. |
| Approach: | They propose a method that allows for end-to-end learning of latent structure predictors jointly with a downstream predictor. |
| Outcome: | The proposed method allows for unrestricted dynamic graph construction from the global latent structure while maintaining differentiability. |
Improving Question Answering with External Knowledge (D19-58)
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| Challenge: | ARC-Easy, ARC Challenge, and OpenBookQA use Wikipedia to augment training data . performance degrades when additional instances exhibit higher difficulty than original training data. |
| Approach: | They propose two methods for exploiting external knowledge for QA in science . they enrich the original corpus with relevant text snippets from an open-domain resource . the second method simply increases the amount of training data by appending additional in-domain instances. |
| Outcome: | The proposed methods achieve gains in accuracy of 8.1%, 13.0%, and 12.8% on science QA tasks. |
Harvesting Paragraph-level Question-Answer Pairs from Wikipedia (P18-1)
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| Challenge: | Existing models that only take into account sentence-level information do not generate question-answer pairs. |
| Approach: | They propose a neural network approach that incorporates coreference knowledge via a novel gating mechanism for paragraphlevel question generation. |
| Outcome: | The proposed model outperforms existing models on a Wikipedia article question-answer generation task. |
Be Consistent! Improving Procedural Text Comprehension using Label Consistency (N19-1)
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| Challenge: | Existing systems for procedural text comprehension still struggle with this task . evaluative work shows that consistent predictions from multiple entities can improve performance . |
| Approach: | They propose a framework that leverages label consistency during training to improve prediction performance. |
| Outcome: | The proposed framework significantly improves prediction performance over previous state-of-the-art systems on a standard benchmark dataset for procedural text, ProPara. |
Leveraging Topic Relatedness for Argument Persuasion (2021.findings-acl)
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| Challenge: | Existing studies of argumentation focus on the effects of factors such as source, audience, and language style, but the impact of exploiting the relationships among controversial topics is under-explored. |
| Approach: | They propose to model topic relatedness among controversial topics using topic embedding features and topic semantics features extracted from the arguments. |
| Outcome: | The proposed method improves predicting persuasiveness and generalizes to rare topics in a few-shot setting. |
Self-Teaching Machines to Read and Comprehend with Large-Scale Multi-Subject Question-Answering Data (2021.findings-emnlp)
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| Challenge: | despite considerable progress, most machine reading comprehension tasks lack sufficient training data to fully exploit powerful deep neural network models. |
| Approach: | They propose to use QA data to generate more training data for machine reading comprehension tasks by crowdsourcing . they first collect a large-scale multiple-choice QA dataset for Chinese, ExamQA, and then use incomplete, yet relevant snippets returned by a web search engine as the context for each QA instance. |
| Outcome: | The proposed model improves a Chinese MRC task with +5.1% accuracy and +3.8% exact match. |
Nested Named Entity Recognition Revisited (N18-1)
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| Challenge: | Existing methods for named entity recognition and mention detection ignore nested entities along with any semantic relations between them. |
| Approach: | They propose a recurrent neural network-based approach to handle named entity recognition and nested entity mention detection simultaneously. |
| Outcome: | The proposed model outperforms state-of-the-art methods on three standard datasets. |
Template Filling with Generative Transformers (2021.naacl-main)
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| Challenge: | Template filling tasks are usually tackled by a pipeline of two separate systems, one for role-filler extraction and another for template/event recognition. |
| Approach: | They propose a framework that naturally models the dependence between entities within a single event and across multiple events described in a document. |
| Outcome: | The proposed framework outperforms pipeline-based approaches and other neural baselines that do not model between-event dependencies on documents containing multiple events. |
Improving Event Duration Prediction via Time-aware Pre-training (2020.findings-emnlp)
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| Challenge: | Understanding duration of event expressed in text is crucial task in NLP . current methods focus on developing features and cannot utilize external textual knowledge. |
| Approach: | They propose two models that incorporate external knowledge by reading temporal-related news sentences. |
| Outcome: | The proposed models outperform baseline models and capture duration information more accurately. |
Probing Representations for Document-level Event Extraction (2023.findings-emnlp)
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| Challenge: | Document-level information extraction tasks require a more comprehensive understanding that often extends to the entire input document. |
| Approach: | They propose to use probing to analyze document-level information extraction representations by embedding probes into a standard dataset. |
| Outcome: | The proposed models improve argument detections but struggle with document length and cross-sentence discourse. |
Keeping Notes: Conditional Natural Language Generation with a Scratchpad Encoder (P19-1)
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| Challenge: | Qualitative assessments in the form of human judgements (question generation), attention visualization (MT), and sample output (summarization) provide further evidence of the ability of Scratchpad to generate fluent and expressive output. |
| Approach: | They propose to use the encoder as a "scratchpad" memory to keep track of what has been generated and guide future generation. |
| Outcome: | The proposed mechanism improves the fluency of seq2seq models on three well-studied natural language generation tasks. |
The Role of Pragmatic and Discourse Context in Determining Argument Impact (D19-1)
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| Challenge: | Recent work shows that attributes of both the audience and communicator constitute important cues for determining argument strength. |
| Approach: | They propose to use a dataset to study the pragmatic and discourse context of argumentative claims to build predictive models that incorporate the pragmatic context of the argument. |
| Outcome: | The proposed models outperform models that rely on claim-specific linguistic features for predicting the perceived impact of individual claims within a particular line of argument. |
WikiLingua: A New Benchmark Dataset for Cross-Lingual Abstractive Summarization (2020.findings-emnlp)
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| Challenge: | a lack of high quality multilingual data for cross-lingual summarization is a costly endeavor since it requires humans to read, comprehend, condense, and paraphrase entire articles. |
| Approach: | They propose to use a large-scale, multilingual dataset to evaluate cross-lingual abstractive summarization systems. |
| Outcome: | The proposed method significantly outperforms baseline approaches while being more cost efficient during inference. |
End-to-end Case-Based Reasoning for Commonsense Knowledge Base Completion (2023.eacl-main)
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| Challenge: | Pretrained language models have been shown to store knowledge in their parameters and have achieved reasonable performance in knowledge-intensive tasks. |
| Approach: | They propose to provide retrieved passages that contain relevant knowledge as additional input to the commonsense knowledge base completion (CKBC) task. |
| Outcome: | The proposed framework generates more valid, informative, and novel knowledge than the state-of-the-art COMET model for commonsense knowledge base completion (CKBC) tasks. |
When in Doubt: Improving Classification Performance with Alternating Normalization (2021.findings-emnlp)
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| Challenge: | a classifier that uses a nonparametric post-processing step for classification suffers when given examples that are close to its decision boundary. |
| Approach: | They propose a nonparametric post-processing step that re-adjusts predicted class probability distributions using high-confidence validation examples. |
| Outcome: | The proposed method improves classifier accuracy on difficult examples. |
A Corpus of eRulemaking User Comments for Measuring Evaluability of Arguments (L18-1)
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| Challenge: | eRulemaking is a way for government agencies to directly reach citizens to solicit their opinions and experiences regarding newly proposed rules. |
| Approach: | They propose an argument mining corpus annotated with argumentative structure information capturing the evaluability of arguments. |
| Outcome: | The proposed corpus contains 731 user comments on consumer debt collection practices rule by the Consumer Financial Protection Bureau. |
Abductive Commonsense Reasoning Exploiting Mutually Exclusive Explanations (2023.acl-long)
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| Challenge: | Existing approaches for abductive reasoning in natural language processing rely on manual supervision. |
| Approach: | They propose an approach for abductive commonsense reasoning that exploits the fact that only a subset of explanations is correct for a given context. |
| Outcome: | The proposed approach outperforms or is comparable to knowledge-augmented zero-shot methods on a diverse set of abductive reasoning datasets. |
Interpreting Pretrained Contextualized Representations via Reductions to Static Embeddings (2020.acl-main)
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| Challenge: | Contextualized representations have become the default for downstream NLP applications. |
| Approach: | They propose a method for converting from contextualized representations to static lookup-table embeddings and apply it to 5 popular pretrained models and 9 sets of pretrained weights. |
| Outcome: | The proposed methods show that pooling over many contexts significantly improves representational quality under intrinsic evaluation. |
Improving Machine Reading Comprehension with Contextualized Commonsense Knowledge (2022.acl-long)
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| Challenge: | Existing methods to improve machine reading comprehension (MRC) tasks require unstated knowledge to perform well. |
| Approach: | They propose to extract a new kind of structured knowledge from scripts and use it to improve machine reading comprehension (MRC) They propose a teacher-student paradigm to facilitate the transfer of knowledge in weakly-labeled MRC data. |
| Outcome: | The proposed method outperforms methods that use weakly-labeled data and improves a state-of-the-art baseline by 4.3% in accuracy on a Chinese multiple-choice MRC dataset C3. |
Investigating Prior Knowledge for Challenging Chinese Machine Reading Comprehension (2020.tacl-1)
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| Challenge: | ''Language is, at best, a means of directing others to construct similar-thoughts from their own prior knowledge,'' says K. S. Adams and Bruce. |
| Approach: | They present a free-form multiple-choice Chinese machine reading Comprehension dataset (C3) containing 13,369 documents and their associated 19,577 multiple-CHOice free- form questions. |
| Outcome: | The proposed model outperforms human models on linguistic, domain-specific, and general world knowledge problems. |
Improving Machine Reading Comprehension with General Reading Strategies (N19-1)
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| Challenge: | Recent studies have shown that reading strategies improve comprehension levels for readers lacking adequate prior knowledge. |
| Approach: | They propose three general strategies to improve machine reading comprehension (MRC) by fine-tuning a pre-trained model with strategies and a target task. |
| Outcome: | The proposed models improve non-extractive machine reading comprehension (MRC) on the largest general domain multiple-choice dataset RACE. |
Corpus Poisoning via Approximate Greedy Gradient Descent (2025.findings-acl)
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| Challenge: | Dense retrievers have been shown to be vulnerable to corpus poisoning attacks . a malicious user injects a small fraction of adversarial passages into the retrieval corpus to trick the system into returning these passages among the top-ranked results for a broad set of user queries. |
| Approach: | They propose an attack on dense retrieval systems based on the widely used HotFlip method for efficiently generating adversarial passages. |
| Outcome: | The proposed method achieves a high success rate on several datasets and using several retrievers, and can generalize to unseen queries and new domains. |