Search Results for author: Sachindra Joshi

Found 41 papers, 14 papers with code

TWEETSUMM - A Dialog Summarization Dataset for Customer Service

no code implementations Findings (EMNLP) 2021 Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov

In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.

Extractive Summarization Unsupervised Extractive Summarization

Neural Conversational QA: Learning to Reason vs Exploiting Patterns

no code implementations EMNLP 2020 Nikhil Verma, Abhishek Sharma, Dhiraj Madan, Danish Contractor, Harshit Kumar, Sachindra Joshi

Neural Conversational QA tasks such as ShARC require systems to answer questions based on the contents of a given passage.

Using Question Answering Rewards to Improve Abstractive Summarization

1 code implementation Findings (EMNLP) 2021 Chulaka Gunasekara, Guy Feigenblat, Benjamin Sznajder, Ranit Aharonov, Sachindra Joshi

Particularly, the results from human evaluations show that the summaries generated by our approach is preferred over 30% of the time over the summaries generated by general abstractive summarization models.

Abstractive Text Summarization Question Answering

BRAIn: Bayesian Reward-conditioned Amortized Inference for natural language generation from feedback

no code implementations4 Feb 2024 Gaurav Pandey, Yatin Nandwani, Tahira Naseem, Mayank Mishra, Guangxuan Xu, Dinesh Raghu, Sachindra Joshi, Asim Munawar, Ramón Fernandez Astudillo

Following the success of Proximal Policy Optimization (PPO) for Reinforcement Learning from Human Feedback (RLHF), new techniques such as Sequence Likelihood Calibration (SLiC) and Direct Policy Optimization (DPO) have been proposed that are offline in nature and use rewards in an indirect manner.

Text Generation

Evaluating Robustness of Dialogue Summarization Models in the Presence of Naturally Occurring Variations

no code implementations15 Nov 2023 Ankita Gupta, Chulaka Gunasekara, Hui Wan, Jatin Ganhotra, Sachindra Joshi, Marina Danilevsky

We find that both fine-tuned and instruction-tuned models are affected by input variations, with the latter being more susceptible, particularly to dialogue-level perturbations.

Evaluating Chatbots to Promote Users' Trust -- Practices and Open Problems

no code implementations9 Sep 2023 Biplav Srivastava, Kausik Lakkaraju, Tarmo Koppel, Vignesh Narayanan, Ashish Kundu, Sachindra Joshi

Chatbots, the common moniker for collaborative assistants, are Artificial Intelligence (AI) software that enables people to naturally interact with them to get tasks done.

Chatbot Language Modelling +1

Pointwise Mutual Information Based Metric and Decoding Strategy for Faithful Generation in Document Grounded Dialogs

1 code implementation20 May 2023 Yatin Nandwani, Vineet Kumar, Dinesh Raghu, Sachindra Joshi, Luis A. Lastras

PMI quantifies the extent to which the document influences the generated response -- with a higher PMI indicating a more faithful response.

Response Generation

Learning as Conversation: Dialogue Systems Reinforced for Information Acquisition

1 code implementation NAACL 2022 Pengshan Cai, Hui Wan, Fei Liu, Mo Yu, Hong Yu, Sachindra Joshi

We propose novel AI-empowered chat bots for learning as conversation where a user does not read a passage but gains information and knowledge through conversation with a teacher bot.

Fast and Light-Weight Answer Text Retrieval in Dialogue Systems

1 code implementation NAACL (ACL) 2022 Hui Wan, Siva Sankalp Patel, J. William Murdock, Saloni Potdar, Sachindra Joshi

Dialogue systems can benefit from being able to search through a corpus of text to find information relevant to user requests, especially when encountering a request for which no manually curated response is available.

Re-Ranking Retrieval +1

Mix-and-Match: Scalable Dialog Response Retrieval using Gaussian Mixture Embeddings

no code implementations6 Apr 2022 Gaurav Pandey, Danish Contractor, Sachindra Joshi

In this paper, we combine the best of both worlds by proposing a scalable model that can learn complex relationships between context-response pairs.

Retrieval

DG2: Data Augmentation Through Document Grounded Dialogue Generation

no code implementations SIGDIAL (ACL) 2022 Qingyang Wu, Song Feng, Derek Chen, Sachindra Joshi, Luis A. Lastras, Zhou Yu

Collecting data for training dialog systems can be extremely expensive due to the involvement of human participants and need for extensive annotation.

Data Augmentation Dialogue Generation

TWEETSUMM -- A Dialog Summarization Dataset for Customer Service

1 code implementation23 Nov 2021 Guy Feigenblat, Chulaka Gunasekara, Benjamin Sznajder, Sachindra Joshi, David Konopnicki, Ranit Aharonov

In most cases, at the end of the conversation, agents are asked to write a short summary emphasizing the problem and the proposed solution, usually for the benefit of other agents that may have to deal with the same customer or issue.

Extractive Summarization Unsupervised Extractive Summarization

MultiDoc2Dial: Modeling Dialogues Grounded in Multiple Documents

1 code implementation EMNLP 2021 Song Feng, Siva Sankalp Patel, Hui Wan, Sachindra Joshi

We propose MultiDoc2Dial, a new task and dataset on modeling goal-oriented dialogues grounded in multiple documents.

Machine Reading Comprehension

End-to-End Learning of Flowchart Grounded Task-Oriented Dialogs

1 code implementation EMNLP 2021 Dinesh Raghu, Shantanu Agarwal, Sachindra Joshi, Mausam

We propose a novel problem within end-to-end learning of task-oriented dialogs (TOD), in which the dialog system mimics a troubleshooting agent who helps a user by diagnosing their problem (e. g., car not starting).

Flowchart Grounded Dialog Response Generation Retrieval +1

Constraint based Knowledge Base Distillation in End-to-End Task Oriented Dialogs

no code implementations Findings (ACL) 2021 Dinesh Raghu, Atishya Jain, Mausam, Sachindra Joshi

In this paper, we propose a novel filtering technique that consists of (1) a pairwise similarity based filter that identifies relevant information by respecting the n-ary structure in a KB record.

Response Generation Task-Oriented Dialogue Systems

Integrating Dialog History into End-to-End Spoken Language Understanding Systems

no code implementations18 Aug 2021 Jatin Ganhotra, Samuel Thomas, Hong-Kwang J. Kuo, Sachindra Joshi, George Saon, Zoltán Tüske, Brian Kingsbury

End-to-end spoken language understanding (SLU) systems that process human-human or human-computer interactions are often context independent and process each turn of a conversation independently.

Intent Recognition Spoken Language Understanding

Explaining Neural Network Predictions on Sentence Pairs via Learning Word-Group Masks

1 code implementation NAACL 2021 Hanjie Chen, Song Feng, Jatin Ganhotra, Hui Wan, Chulaka Gunasekara, Sachindra Joshi, Yangfeng Ji

Most existing methods generate post-hoc explanations for neural network models by identifying individual feature attributions or detecting interactions between adjacent features.

Natural Language Inference Paraphrase Identification +1

Does Dialog Length matter for Next Response Selection task? An Empirical Study

no code implementations24 Jan 2021 Jatin Ganhotra, Sachindra Joshi

However, there has been little to no research on the impact of this limitation with respect to dialog tasks.

doc2dial: A Goal-Oriented Document-Grounded Dialogue Dataset

2 code implementations EMNLP 2020 Song Feng, Hui Wan, Chulaka Gunasekara, Siva Sankalp Patel, Sachindra Joshi, Luis A. Lastras

We introduce doc2dial, a new dataset of goal-oriented dialogues that are grounded in the associated documents.

Simulated Chats for Building Dialog Systems: Learning to Generate Conversations from Instructions

no code implementations Findings (EMNLP) 2021 Biswesh Mohapatra, Gaurav Pandey, Danish Contractor, Sachindra Joshi

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.

Dialog Learning Language Modelling

Effects of Naturalistic Variation in Goal-Oriented Dialog

1 code implementation Findings of the Association for Computational Linguistics 2020 Jatin Ganhotra, Robert Moore, Sachindra Joshi, Kahini Wadhawan

Existing benchmarks used to evaluate the performance of end-to-end neural dialog systems lack a key component: natural variation present in human conversations.

Goal-Oriented Dialog

Mask & Focus: Conversation Modelling by Learning Concepts

no code implementations11 Feb 2020 Gaurav Pandey, Dinesh Raghu, Sachindra Joshi

The proposed model, referred to as Mask \& Focus maps the input context to a sequence of concepts which are then used to generate the response concepts.

Machine Translation Response Generation

A Practical Dialogue-Act-Driven Conversation Model for Multi-Turn Response Selection

no code implementations IJCNLP 2019 Harshit Kumar, Arvind Agarwal, Sachindra Joshi

This paper proposes an end-to-end multi-task model for conversation modeling, which is optimized for two tasks, dialogue act prediction and response selection, with the latter being the task of interest.

Neural Conversational QA: Learning to Reason v.s. Exploiting Patterns

2 code implementations9 Sep 2019 Nikhil Verma, Abhishek Sharma, Dhiraj Madan, Danish Contractor, Harshit Kumar, Sachindra Joshi

On studying recent state-of-the-art models on the ShARCQA task, we found indications that the models learn spurious clues/patterns in the dataset.

Unsupervised Learning of Interpretable Dialog Models

no code implementations2 Nov 2018 Dhiraj Madan, Dinesh Raghu, Gaurav Pandey, Sachindra Joshi

However these states need to be handcrafted and annotated in the data.

Multi-level Memory for Task Oriented Dialogs

1 code implementation NAACL 2019 Revanth Reddy, Danish Contractor, Dinesh Raghu, Sachindra Joshi

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.

Dialogue-act-driven Conversation Model : An Experimental Study

no code implementations COLING 2018 Harshit Kumar, Arvind Agarwal, Sachindra Joshi

The utility of additional semantic information for the task of next utterance selection in an automated dialogue system is the focus of study in this paper.

Dialogue Generation

Styling with Attention to Details

no code implementations3 Jul 2018 Ayushi Dalmia, Sachindra Joshi, Raghavendra Singh, Vikas Raykar

We pose the problem of predicting complementary fashion items as a sequence to sequence problem where the input is the selected set of fashion items and the output is a complementary fashion item based on the style information learned by the model.

Retrieval

Exemplar Encoder-Decoder for Neural Conversation Generation

no code implementations ACL 2018 P, Gaurav ey, Danish Contractor, Vineet Kumar, Sachindra Joshi

In this paper we present the Exemplar Encoder-Decoder network (EED), a novel conversation model that learns to utilize \textit{similar} examples from training data to generate responses.

Retrieval

Finding Dominant User Utterances And System Responses in Conversations

no code implementations IJCNLP 2017 Dhiraj Madan, Sachindra Joshi

We propose a novel SimCluster algorithm that extends standard K-means algorithm to simultaneously cluster user utterances and agent utterances by taking their adjacency information into account.

Clustering

Author-Specific Sentiment Aggregation for Polarity Prediction of Reviews

no code implementations LREC 2014 Subhabrata Mukherjee, Sachindra Joshi

Furthermore, we also show the effectiveness of our approach in capturing thwarting in reviews, achieving an accuracy improvement of 11. 53{\%} over the SVM baseline.

Dependency Parsing General Classification +2

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