Anutosh Maitra


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Sentiment Aided Graph Attentive Contextualization for Task Oriented Negotiation Dialogue Generation
Aritra Raut | Sriparna Saha | Anutosh Maitra | Roshni Ramnani
Proceedings of the 13th International Joint Conference on Natural Language Processing and the 3rd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics (Volume 1: Long Papers)

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INA: An Integrative Approach for Enhancing Negotiation Strategies with Reward-Based Dialogue Agent
Zishan Ahmad | Suman Saurabh | Vaishakh Menon | Asif Ekbal | Roshni Ramnani | Anutosh Maitra
Findings of the Association for Computational Linguistics: EMNLP 2023

In this paper, we propose a novel negotiation agent designed for the online marketplace. Our dialogue agent is integrative in nature i.e, it possesses the capability to negotiate on price as well as other factors, such as the addition or removal of items from a deal bundle, thereby offering a more flexible and comprehensive negotiation experience. To enable this functionality, we create a new dataset called Integrative Negotiation Dataset (IND). For this dataset creation, we introduce a new semi-automated data creation method, which combines defining negotiation intents, actions, and intent-action simulation between users and the agent to generate potential dialogue flows. Finally, the prompting of GPT-J, a state-of-the-art language model, is done to generate dialogues for a given intent, with a human-in-the-loop process for post-editing and refining minor errors to ensure high data quality. We first train a maximum likelihood loss based model on IND, and then employ a set of novel rewards specifically tailored for the negotiation task to train our Integrative Negotiation Agent (INA). These rewards incentivize the agent to learn effective negotiation strategies that can adapt to various contextual requirements and price proposals. We train our model and conduct experiments to evaluate the effectiveness of our reward-based dialogue agent for negotiation. Our results demonstrate that the proposed approach and reward functions significantly enhance the negotiation capabilities of the dialogue agent. The INA successfully engages in integrative negotiations, displaying the ability to dynamically adjust prices and negotiate the inclusion or exclusion of items in a deal bundle.


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Persona or Context? Towards Building Context adaptive Personalized Persuasive Virtual Sales Assistant
Abhisek Tiwari | Sriparna Saha | Shubhashis Sengupta | Anutosh Maitra | Roshni Ramnani | Pushpak Bhattacharyya
Proceedings of the 2nd Conference of the Asia-Pacific Chapter of the Association for Computational Linguistics and the 12th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

Task-oriented conversational agents are gaining immense popularity and success in a wide range of tasks, from flight ticket booking to online shopping. However, the existing systems presume that end-users will always have a pre-determined and servable task goal, which results in dialogue failure in hostile scenarios, such as goal unavailability. On the other hand, human agents accomplish users’ tasks even in a large number of goal unavailability scenarios by persuading them towards a very similar and servable goal. Motivated by the limitation, we propose and build a novel end-to-end multi-modal persuasive dialogue system incorporated with a personalized persuasive module aided goal controller and goal persuader. The goal controller recognizes goal conflicting/unavailability scenarios and formulates a new goal, while the goal persuader persuades users using a personalized persuasive strategy identified through dialogue context. We also present a novel automatic evaluation metric called Persuasiveness Measurement Rate (PMeR) for quantifying the persuasive capability of a conversational agent. The obtained improvements (both quantitative and qualitative) firmly establish the superiority and need of the proposed context-guided, personalized persuasive virtual agent over existing traditional task-oriented virtual agents. Furthermore, we also curated a multi-modal persuasive conversational dialogue corpus annotated with intent, slot, sentiment, and dialogue act for e-commerce domain.

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Hollywood Identity Bias Dataset: A Context Oriented Bias Analysis of Movie Dialogues
Sandhya Singh | Prapti Roy | Nihar Sahoo | Niteesh Mallela | Himanshu Gupta | Pushpak Bhattacharyya | Milind Savagaonkar | Nidhi Sultan | Roshni Ramnani | Anutosh Maitra | Shubhashis Sengupta
Proceedings of the Thirteenth Language Resources and Evaluation Conference

Movies reflect society and also hold power to transform opinions. Social biases and stereotypes present in movies can cause extensive damage due to their reach. These biases are not always found to be the need of storyline but can creep in as the author’s bias. Movie production houses would prefer to ascertain that the bias present in a script is the story’s demand. Today, when deep learning models can give human-level accuracy in multiple tasks, having an AI solution to identify the biases present in the script at the writing stage can help them avoid the inconvenience of stalled release, lawsuits, etc. Since AI solutions are data intensive and there exists no domain specific data to address the problem of biases in scripts, we introduce a new dataset of movie scripts that are annotated for identity bias. The dataset contains dialogue turns annotated for (i) bias labels for seven categories, viz., gender, race/ethnicity, religion, age, occupation, LGBTQ, and other, which contains biases like body shaming, personality bias, etc. (ii) labels for sensitivity, stereotype, sentiment, emotion, emotion intensity, (iii) all labels annotated with context awareness, (iv) target groups and reason for bias labels and (v) expert-driven group-validation process for high quality annotations. We also report various baseline performances for bias identification and category detection on our dataset.


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Can Taxonomy Help? Improving Semantic Question Matching using Question Taxonomy
Deepak Gupta | Rajkumar Pujari | Asif Ekbal | Pushpak Bhattacharyya | Anutosh Maitra | Tom Jain | Shubhashis Sengupta
Proceedings of the 27th International Conference on Computational Linguistics

In this paper, we propose a hybrid technique for semantic question matching. It uses a proposed two-layered taxonomy for English questions by augmenting state-of-the-art deep learning models with question classes obtained from a deep learning based question classifier. Experiments performed on three open-domain datasets demonstrate the effectiveness of our proposed approach. We achieve state-of-the-art results on partial ordering question ranking (POQR) benchmark dataset. Our empirical analysis shows that coupling standard distributional features (provided by the question encoder) with knowledge from taxonomy is more effective than either deep learning or taxonomy-based knowledge alone.