Diane Litman

AAAI Conference 2020 Conference Paper

Entrainment2Vec: Embedding Entrainment for Multi-Party Dialogues

• Zahra Rahimi
• Diane Litman

Entrainment is the propensity of speakers to begin behaving like one another in conversation. While most entrainment studies have focused on dyadic interactions, researchers have also started to investigate multi-party conversations. In these studies, multi-party entrainment has typically been estimated by averaging the pairs’ entrainment values or by averaging individuals’ entrainment to the group. While such multi-party measures utilize the strength of dyadic entrainment, they have not yet exploited different aspects of the dynamics of entrainment relations in multi-party groups. In this paper, utilizing an existing pairwise asymmetric entrainment measure, we propose a novel graph-based vector representation of multi-party entrainment that incorporates both strength and dynamics of pairwise entrainment relations. The proposed kernel approach and weakly-supervised representation learning method show promising results at the downstream task of predicting team outcomes. Also, examining the embedding, we found interesting information about the dynamics of the entrainment relations. For example, teams with more influential members have more process conflict.

AAAI Conference 2018 Conference Paper

Argument Mining for Improving the Automated Scoring of Persuasive Essays

• Huy Nguyen
• Diane Litman

End-to-end argument mining has enabled the development of new automated essay scoring (AES) systems that use argumentative features (e. g. , number of claims, number of support relations) in addition to traditional legacy features (e. g. , grammar, discourse structure) when scoring persuasive essays. While prior research has proposed different argumentative features as well as empirically demonstrated their utility for AES, these studies have all had important limitations. In this paper we identify a set of desiderata for evaluating the use of argument mining for AES, introduce an end-to-end argument mining system and associated argumentative feature sets, and present the results of several studies that both satisfy the desiderata and demonstrate the value-added of argument mining for scoring persuasive essays.

AAAI Conference 2017 Conference Paper

Using Discourse Signals for Robust Instructor Intervention Prediction

• Muthu Kumar Chandrasekaran
• Carrie Epp
• Min-Yen Kan
• Diane Litman

We tackle the prediction of instructor intervention in student posts from discussion forums in Massive Open Online Courses (MOOCs). Our key finding is that using automatically obtained discourse relations improves the prediction of when instructors intervene in student discussions, when compared with a state-of-the-art, feature-rich baseline. Our supervised classifier makes use of an automatic discourse parser which outputs Penn Discourse Treebank (PDTB) tags that represent in-post discourse features. We show PDTB relationbased features increase the robustness of the classifier and complement baseline features in recalling more diverse instructor intervention patterns. In comprehensive experiments over 14 MOOC offerings from several disciplines, the PDTB discourse features improve performance on average. The resultant models are less dependent on domain-specific vocabulary, allowing them to better generalize to new courses.

AAAI Conference 2016 Conference Paper

Natural Language Processing for Enhancing Teaching and Learning

• Diane Litman

NeurIPS Conference 1999 Conference Paper

Reinforcement Learning for Spoken Dialogue Systems

• Satinder Singh
• Michael Kearns
• Diane Litman
• Marilyn Walker

Recently, a number of authors have proposed treating dialogue systems as Markov decision processes (MDPs). However, the practical application ofMDP algorithms to dialogue systems faces a number of severe technical challenges. We have built a general software tool (RLDS, for Reinforcement Learning for Dialogue Systems) based on the MDP framework, and have applied it to dialogue corpora gathered from two dialogue systems built at AT&T Labs. Our experiments demonstrate that RLDS holds promise as a tool for "browsing" and understanding correlations in complex, temporally dependent dialogue corpora.

AAAI Conference 1996 Conference Paper

Path-Based Rules in Object-Oriented Programming

• James M. Crawford
• Diane Litman

Object-oriented programming has recently emerged as one of the most important programming paradigms. While object-oriented programming clearly owes an intellectual debt to AI, it appears to be displacing some AI techniques, such as rule-based programming, from the marketplace. This need not be so as path-based rules-forward-chaining production rules that are restricted to follow pointers between objects-fit into the object-oriented paradigm in a clean and elegant way. The combination of path-based rules and object-oriented programming should be useful in AI applications, and in the more general problem of transferring AI techniques to the larger computer science community.