Satinder Singh


pdf bib
NE-Table: A Neural key-value table for Named Entities
Janarthanan Rajendran | Jatin Ganhotra | Xiaoxiao Guo | Mo Yu | Satinder Singh | Lazaros Polymenakos
Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP 2019)

Many Natural Language Processing (NLP) tasks depend on using Named Entities (NEs) that are contained in texts and in external knowledge sources. While this is easy for humans, the present neural methods that rely on learned word embeddings may not perform well for these NLP tasks, especially in the presence of Out-Of-Vocabulary (OOV) or rare NEs. In this paper, we propose a solution for this problem, and present empirical evaluations on: a) a structured Question-Answering task, b) three related Goal-Oriented dialog tasks, and c) a Reading-Comprehension task, which show that the proposed method can be effective in dealing with both in-vocabulary and OOV NEs. We create extended versions of dialog bAbI tasks 1,2 and 4 and OOV versions of the CBT test set which are available at -


pdf bib
Learning End-to-End Goal-Oriented Dialog with Multiple Answers
Janarthanan Rajendran | Jatin Ganhotra | Satinder Singh | Lazaros Polymenakos
Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing

In a dialog, there could be multiple valid next utterances at any point. The present end-to-end neural methods for dialog do not take this into account. They learn with the assumption that at any time there is only one correct next utterance. In this work, we focus on this problem in the goal-oriented dialog setting where there are different paths to reach a goal. We propose a new method, that uses a combination of supervised learning and reinforcement learning approaches to address this issue. We also propose a new and more effective testbed, permuted-bAbI dialog tasks, by introducing multiple valid next utterances to the original-bAbI dialog tasks, which allows evaluation of end-to-end goal-oriented dialog systems in a more realistic setting. We show that there is a significant drop in performance of existing end-to-end neural methods from 81.5% per-dialog accuracy on original-bAbI dialog tasks to 30.3% on permuted-bAbI dialog tasks. We also show that our proposed method improves the performance and achieves 47.3% per-dialog accuracy on permuted-bAbI dialog tasks. We also release permuted-bAbI dialog tasks, our proposed testbed, to the community for evaluating dialog systems in a goal-oriented setting.


pdf bib
Understanding and Predicting Empathic Behavior in Counseling Therapy
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the 55th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Counselor empathy is associated with better outcomes in psychology and behavioral counseling. In this paper, we explore several aspects pertaining to counseling interaction dynamics and their relation to counselor empathy during motivational interviewing encounters. Particularly, we analyze aspects such as participants’ engagement, participants’ verbal and nonverbal accommodation, as well as topics being discussed during the conversation, with the final goal of identifying linguistic and acoustic markers of counselor empathy. We also show how we can use these findings alongside other raw linguistic and acoustic features to build accurate counselor empathy classifiers with accuracies of up to 80%.

pdf bib
Predicting Counselor Behaviors in Motivational Interviewing Encounters
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An | Kathy J. Goggin | Delwyn Catley
Proceedings of the 15th Conference of the European Chapter of the Association for Computational Linguistics: Volume 1, Long Papers

As the number of people receiving psycho-therapeutic treatment increases, the automatic evaluation of counseling practice arises as an important challenge in the clinical domain. In this paper, we address the automatic evaluation of counseling performance by analyzing counselors’ language during their interaction with clients. In particular, we present a model towards the automation of Motivational Interviewing (MI) coding, which is the current gold standard to evaluate MI counseling. First, we build a dataset of hand labeled MI encounters; second, we use text-based methods to extract and analyze linguistic patterns associated with counselor behaviors; and third, we develop an automatic system to predict these behaviors. We introduce a new set of features based on semantic information and syntactic patterns, and show that they lead to accuracy figures of up to 90%, which represent a significant improvement with respect to features used in the past.


pdf bib
Building a Motivational Interviewing Dataset
Verónica Pérez-Rosas | Rada Mihalcea | Kenneth Resnicow | Satinder Singh | Lawrence An
Proceedings of the Third Workshop on Computational Linguistics and Clinical Psychology


pdf bib
Computationally Rational Saccadic Control: An Explanation of Spillover Effects Based on Sampling from Noisy Perception and Memory
Michael Shvartsman | Richard Lewis | Satinder Singh
Proceedings of the Fifth Workshop on Cognitive Modeling and Computational Linguistics


pdf bib
Automatic Optimization of Dialogue Management
Diane J. Litman | Michael S. Kearns | Satinder Singh | Marilyn A. Walker
COLING 2000 Volume 1: The 18th International Conference on Computational Linguistics

pdf bib
NJFun- A Reinforcement Learning Spoken Dialogue System
Diane Litman | Satinder Singh | Michael Kearns | Marilyn Walker
ANLP-NAACL 2000 Workshop: Conversational Systems