Danish Contractor


2021

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Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions
Biswesh Mohapatra | Gaurav Pandey | Danish Contractor | Sachindra Joshi
Findings of the Association for Computational Linguistics: EMNLP 2021

Popular dialog datasets such as MultiWOZ are created by providing crowd workers an instruction, expressed in natural language, that describes the task to be accomplished. Crowd workers play the role of a user and an agent to generate dialogs to accomplish tasks involving booking restaurant tables, calling a taxi etc. In this paper, we present a data creation strategy that uses the pre-trained language model, GPT2, to simulate the interaction between crowd workers by creating a user bot and an agent bot. We train the simulators using a smaller percentage of actual crowd-generated conversations and their corresponding instructions. We demonstrate that by using the simulated data, we achieve significant improvements in low-resource settings on two publicly available datasets - MultiWOZ dataset and the Persona chat dataset.

2020

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Neural Conversational QA: Learning to Reason vs Exploiting Patterns
Nikhil Verma | Abhishek Sharma | Dhiraj Madan | Danish Contractor | Harshit Kumar | Sachindra Joshi
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage. On studying recent state-of-the-art models on the ShARC QA task, we found indications that the model(s) learn spurious clues/patterns in the data-set. Further, a heuristic-based program, built to exploit these patterns, had comparative performance to that of the neural models. In this paper we share our findings about the four types of patterns in the ShARC corpus and how the neural models exploit them. Motivated by the above findings, we create and share a modified data-set that has fewer spurious patterns than the original data-set, consequently allowing models to learn better.

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Agent Assist through Conversation Analysis
Kshitij Fadnis | Nathaniel Mills | Jatin Ganhotra | Haggai Roitman | Gaurav Pandey | Doron Cohen | Yosi Mass | Shai Erera | Chulaka Gunasekara | Danish Contractor | Siva Patel | Q. Vera Liao | Sachindra Joshi | Luis Lastras | David Konopnicki
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing: System Demonstrations

Customer support agents play a crucial role as an interface between an organization and its end-users. We propose CAIRAA: Conversational Approach to Information Retrieval for Agent Assistance, to reduce the cognitive workload of support agents who engage with users through conversation systems. CAIRAA monitors an evolving conversation and recommends both responses and URLs of documents the agent can use in replies to their client. We combine traditional information retrieval (IR) approaches with more recent Deep Learning (DL) models to ensure high accuracy and efficient run-time performance in the deployed system. Here, we describe the CAIRAA system and demonstrate its effectiveness in a pilot study via a short video.

2019

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Multi-Level Memory for Task Oriented Dialogs
Revanth Gangi Reddy | Danish Contractor | Dinesh Raghu | Sachindra Joshi
Proceedings of the 2019 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, Volume 1 (Long and Short Papers)

Recent end-to-end task oriented dialog systems use memory architectures to incorporate external knowledge in their dialogs. Current work makes simplifying assumptions about the structure of the knowledge base, such as the use of triples to represent knowledge, and combines dialog utterances (context) as well as knowledge base (KB) results as part of the same memory. This causes an explosion in the memory size, and makes the reasoning over memory harder. In addition, such a memory design forces hierarchical properties of the data to be fit into a triple structure of memory. This requires the memory reader to infer relationships across otherwise connected attributes. In this paper we relax the strong assumptions made by existing architectures and separate memories used for modeling dialog context and KB results. Instead of using triples to store KB results, we introduce a novel multi-level memory architecture consisting of cells for each query and their corresponding results. The multi-level memory first addresses queries, followed by results and finally each key-value pair within a result. We conduct detailed experiments on three publicly available task oriented dialog data sets and we find that our method conclusively outperforms current state-of-the-art models. We report a 15-25% increase in both entity F1 and BLEU scores.

2018

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Exemplar Encoder-Decoder for Neural Conversation Generation
Gaurav Pandey | Danish Contractor | Vineet Kumar | Sachindra Joshi
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize similar examples from training data to generate responses. Similar conversation examples (context-response pairs) from training data are retrieved using a traditional TF-IDF based retrieval model and the corresponding responses are used by our decoder to generate the ground truth response. The contribution of each retrieved response is weighed by the similarity of corresponding context with the input context. As a result, our model learns to assign higher similarity scores to those retrieved contexts whose responses are crucial for generating the final response. We present detailed experiments on two large data sets and we find that our method out-performs state of the art sequence to sequence generative models on several recently proposed evaluation metrics.

2016

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Entity-balanced Gaussian pLSA for Automated Comparison
Danish Contractor | Parag Singla | Mausam
Proceedings of the 2016 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

2012

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Using Argumentative Zones for Extractive Summarization of Scientific Articles
Danish Contractor | Yufan Guo | Anna Korhonen
Proceedings of COLING 2012

2011

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Labeling Unlabeled Data using Cross-Language Guided Clustering
Sachindra Joshi | Danish Contractor | Sumit Negi
Proceedings of 5th International Joint Conference on Natural Language Processing

2010

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Unsupervised cleansing of noisy text
Danish Contractor | Tanveer A. Faruquie | L. Venkata Subramaniam
Coling 2010: Posters

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Handling Noisy Queries in Cross Language FAQ Retrieval
Danish Contractor | Govind Kothari | Tanveer Faruquie | L. V. Subramaniam | Sumit Negi
Proceedings of the 2010 Conference on Empirical Methods in Natural Language Processing