Lise Getoor


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CausalDialogue: Modeling Utterance-level Causality in Conversations
Yi-Lin Tuan | Alon Albalak | Wenda Xu | Michael Saxon | Connor Pryor | Lise Getoor | William Yang Wang
Findings of the Association for Computational Linguistics: ACL 2023

Despite their widespread adoption, neural conversation models have yet to exhibit natural chat capabilities with humans. In this research, we examine user utterances as causes and generated responses as effects, recognizing that changes in a cause should produce a different effect. To further explore this concept, we have compiled and expanded upon a new dataset called CausalDialogue through crowd-sourcing. This dataset includes multiple cause-effect pairs within a directed acyclic graph (DAG) structure. Our analysis reveals that traditional loss functions struggle to effectively incorporate the DAG structure, leading us to propose a causality-enhanced method called Exponential Maximum Average Treatment Effect (ExMATE) to enhance the impact of causality at the utterance level in training neural conversation models. To evaluate the needs of considering causality in dialogue generation, we built a comprehensive benchmark on CausalDialogue dataset using different models, inference, and training methods. Through experiments, we find that a causality-inspired loss like ExMATE can improve the diversity and agility of conventional loss function and there is still room for improvement to reach human-level quality on this new dataset.

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Using Domain Knowledge to Guide Dialog Structure Induction via Neural Probabilistic Soft Logic
Connor Pryor | Quan Yuan | Jeremiah Liu | Mehran Kazemi | Deepak Ramachandran | Tania Bedrax-Weiss | Lise Getoor
Proceedings of the 61st Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Dialog Structure Induction (DSI) is the task of inferring the latent dialog structure (i.e., a set of dialog states and their temporal transitions) of a given goal-oriented dialog. It is a critical component for modern dialog system design and discourse analysis. Existing DSI approaches are often purely data-driven, deploy models that infer latent states without access to domain knowledge, underperform when the training corpus is limited/noisy, or have difficulty when test dialogs exhibit distributional shifts from the training domain. This work explores a neural-symbolic approach as a potential solution to these problems. We introduce Neural Probabilistic Soft Logic Dialogue Structure Induction (NEUPSL DSI), a principled approach that injects symbolic knowledge into the latent space of a generative neural model. We conduct a thorough empirical investigation on the effect of NEUPSL DSI learning on hidden representation quality, few-shot learning, and out-of-domain generalization performance. Over three dialog structure induction datasets and across unsupervised and semi-supervised settings for standard and cross-domain generalization, the injection of symbolic knowledge using NEUPSL DSI provides a consistent boost in performance over the canonical baselines.


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D-REX: Dialogue Relation Extraction with Explanations
Alon Albalak | Varun Embar | Yi-Lin Tuan | Lise Getoor | William Yang Wang
Proceedings of the 4th Workshop on NLP for Conversational AI

Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods. This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations. We propose our model-agnostic framework, D-REX, a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously. We frame relation extraction as a re-ranking task and include relation- and entity-specific explanations as an intermediate step of the inference process. We find that human annotators are 4.2 times more likely to prefer D-REX’s explanations over a joint relation extraction and explanation model. Finally, our evaluations show that D-REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.

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FETA: A Benchmark for Few-Sample Task Transfer in Open-Domain Dialogue
Alon Albalak | Yi-Lin Tuan | Pegah Jandaghi | Connor Pryor | Luke Yoffe | Deepak Ramachandran | Lise Getoor | Jay Pujara | William Yang Wang
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Task transfer, transferring knowledge contained in related tasks, holds the promise of reducing the quantity of labeled data required to fine-tune language models. Dialogue understanding encompasses many diverse tasks, yet task transfer has not been thoroughly studied in conversational AI. This work explores conversational task transfer by introducing FETA: a benchmark for FEw-sample TAsk transfer in open-domain dialogue.FETA contains two underlying sets of conversations upon which there are 10 and 7 tasks annotated, enabling the study of intra-dataset task transfer; task transfer without domain adaptation. We utilize three popular language models and three learning algorithms to analyze the transferability between 132 source-target task pairs and create a baseline for future work. We run experiments in the single- and multi-source settings and report valuable findings, e.g., most performance trends are model-specific, and span extraction and multiple-choice tasks benefit the most from task transfer. In addition to task transfer, FETA can be a valuable resource for future research into the efficiency and generalizability of pre-training datasets and model architectures, as well as for learning settings such as continual and multitask learning.


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Sparsity and Noise: Where Knowledge Graph Embeddings Fall Short
Jay Pujara | Eriq Augustine | Lise Getoor
Proceedings of the 2017 Conference on Empirical Methods in Natural Language Processing

Knowledge graph (KG) embedding techniques use structured relationships between entities to learn low-dimensional representations of entities and relations. One prominent goal of these approaches is to improve the quality of knowledge graphs by removing errors and adding missing facts. Surprisingly, most embedding techniques have been evaluated on benchmark datasets consisting of dense and reliable subsets of human-curated KGs, which tend to be fairly complete and have few errors. In this paper, we consider the problem of applying embedding techniques to KGs extracted from text, which are often incomplete and contain errors. We compare the sparsity and unreliability of different KGs and perform empirical experiments demonstrating how embedding approaches degrade as sparsity and unreliability increase.


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RELLY: Inferring Hypernym Relationships Between Relational Phrases
Adam Grycner | Gerhard Weikum | Jay Pujara | James Foulds | Lise Getoor
Proceedings of the 2015 Conference on Empirical Methods in Natural Language Processing

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Weakly Supervised Models of Aspect-Sentiment for Online Course Discussion Forums
Arti Ramesh | Shachi H. Kumar | James Foulds | Lise Getoor
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

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Joint Models of Disagreement and Stance in Online Debate
Dhanya Sridhar | James Foulds | Bert Huang | Lise Getoor | Marilyn Walker
Proceedings of the 53rd Annual Meeting of the Association for Computational Linguistics and the 7th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)


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Understanding MOOC Discussion Forums using Seeded LDA
Arti Ramesh | Dan Goldwasser | Bert Huang | Hal Daumé | Lise Getoor
Proceedings of the Ninth Workshop on Innovative Use of NLP for Building Educational Applications

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Collective Stance Classification of Posts in Online Debate Forums
Dhanya Sridhar | Lise Getoor | Marilyn Walker
Proceedings of the Joint Workshop on Social Dynamics and Personal Attributes in Social Media


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Supervised and Unsupervised Methods in Employing Discourse Relations for Improving Opinion Polarity Classification
Swapna Somasundaran | Galileo Namata | Janyce Wiebe | Lise Getoor
Proceedings of the 2009 Conference on Empirical Methods in Natural Language Processing

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Opinion Graphs for Polarity and Discourse Classification
Swapna Somasundaran | Galileo Namata | Lise Getoor | Janyce Wiebe
Proceedings of the 2009 Workshop on Graph-based Methods for Natural Language Processing (TextGraphs-4)


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Unsupervised Sense Disambiguation Using Bilingual Probabilistic Models
Indrajit Bhattacharya | Lise Getoor | Yoshua Bengio
Proceedings of the 42nd Annual Meeting of the Association for Computational Linguistics (ACL-04)