Papers by Jeffrey Flanigan
Diverse Retrieval-Augmented In-Context Learning for Dialogue State Tracking (2023.findings-acl)
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| Challenge: | Recent work has demonstrated that in-context learning for dialogue state tracking outperforms training methods in the few-shot setting. |
| Approach: | They propose a method for in-context learning for dialogue state tracking that takes into account probabilities of competing surface forms and produces a more accurate dialogue state prediction. |
| Outcome: | The proposed method outperforms trained methods in the few-shot setting and requires little data and zero parameter updates. |
LiDARR: Linking Document AMRs with Referents Resolvers (2025.acl-demo)
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Jon Cai, Kristin Wright-Bettner, Zekun Zhao, Shafiuddin Rehan Ahmed, Abijith Trichur Ramachandran, Jeffrey Flanigan, Martha Palmer, James Martin
| Challenge: | Abstract Meaning Representation (AMR) is a formalism for semantic representation of natural language text. |
| Approach: | They propose a web tool for semantic annotation at the document level using Abstract Meaning Representation (AMR) it integrates an AMR-to-surface alignment model and a coreference resolution model into the tool . |
| Outcome: | The proposed tool simplifies the creation of knowledge graphs from natural language documents . it integrates an AMR-to-surface alignment model and coreference resolution model . |
In Search of the Lost Arch in Dialogue: A Dependency Dialogue Acts Corpus for Multi-Party Dialogues (2025.findings-acl)
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Jon Cai, Brendan King, Peyton Cameron, Susan Windisch Brown, Miriam Eckert, Dananjay Srinivas, George Arthur Baker, V Kate Everson, Martha Palmer, James Martin, Jeffrey Flanigan
| Challenge: | Understanding speaker intentions remains a challenge in NLP . a number of corpora annotated using theoretical frameworks of dialogue focus on utterance-level labeling of speaker intent, missing wider context, or the rhetorical structure of a dialogue. |
| Approach: | They propose to annotate a corpus of 33 dialogues and over 9,000 utterance units using the Dependency Dialogue Acts framework. |
| Outcome: | The proposed corpus spans four genres of multi-party conversations from different modalities. |
SYNTHVERIFY: Enhancing Zero-Shot Claim Verification through Step-by-Step Synthetic Data Generation (2025.findings-acl)
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| Challenge: | Existing methods for claim verification are inefficient or rely on external documents. |
| Approach: | They propose a step-by-step prompting-based synthetic data generation framework to enhance zero-shot claim verification. |
| Outcome: | The proposed framework bridges LLMs’ knowledge gaps in specialized domains without access to external corpora or sacrificing generalizability. |
RAC: Efficient LLM Factuality Correction with Retrieval Augmentation (2025.findings-emnlp)
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| Challenge: | Large Language Models (LLMs) exhibit impressive results across a wide range of tasks, yet they can often produce factually incorrect outputs. |
| Approach: | They propose a low-latency post-correction method that decomposes the LLM’s output into atomic facts and applies a fine-grained verification and correction process with retrieved content to verify and correct the Llm-generated output. |
| Outcome: | The proposed method has greatly reduced latency and token consumption up to 7x compared to previous state-of-the-art methods with similar or better performance. |
Avoiding Overlap in Data Augmentation for AMR-to-Text Generation (2021.acl-short)
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| Challenge: | Using unlabeled data to boost model performance is common practice in machine learning and natural language processing. |
| Approach: | They propose methods for excluding parts of Gigaword to remove overlap . they propose to use the AMR dataset for AMR-to-text generation . |
| Outcome: | The proposed approach leads to a more realistic evaluation of the task of AMR-to-text generation. |
DocAMR: Multi-Sentence AMR Representation and Evaluation (2022.naacl-main)
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Tahira Naseem, Austin Blodgett, Sadhana Kumaravel, Tim O’Gorman, Young-Suk Lee, Jeffrey Flanigan, Ramón Astudillo, Radu Florian, Salim Roukos, Nathan Schneider
| Challenge: | Abstract Meaning Representation (AMR) graphs are compared to gold graphs by the Smatch metric, but lack a well-defined representation and evaluation. |
| Approach: | They propose an algorithm for deriving a unified graph representation using a super-sentential annotation method. |
| Outcome: | The proposed algorithm avoids the pitfalls of over-merging and lacks coherence from under merging. |
Meaning Representations for Natural Languages: Design, Models and Applications (2022.emnlp-tutorials)
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| Challenge: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Unsupervised End-to-End Task-Oriented Dialogue with LLMs: The Power of the Noisy Channel (2024.emnlp-main)
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| Challenge: | a task-oriented dialogue system requires turn-level annotations for interacting with their APIs. |
| Approach: | They propose an unsupervised approach that infers turn-level annotations as latent variables using a noisy channel model to build an end-to-end dialogue agent. |
| Outcome: | The proposed method doubles the success rate of a strong GPT-3.5 benchmark. |
Meaning Representations for Natural Languages: Design, Models and Applications (2024.lrec-tutorials)
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| Challenge: | a tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. |
| Approach: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation. authors propose a cutting-edge, full-day tutorial for all stakeholders in the AI community. |
| Outcome: | This tutorial reviews the design of common meaning representations and SoTA models for predicting meaning representation models . it also reviews the applications of meaning representation in downstream NLP tasks and real-world applications . |
Automatic Identification of Code-Switching Functions in Speech Transcripts (2023.findings-acl)
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| Challenge: | Code-switching, or switching between languages, occurs for many reasons and has important linguistic, sociological, and cultural implications. |
| Approach: | They build a system to identify a wide range of functions for which speakers code-switch in everyday speech with an accuracy of 75% . they use a dataset of Hindi-English code-witched data to analyze their results . |
| Outcome: | The proposed system can identify a wide range of functions for which speakers code-switch in everyday speech, with an accuracy of 75% across all functions. |