Search Results for author: Bodhisattwa Prasad Majumder

Found 25 papers, 13 papers with code

Interview: Large-scale Modeling of Media Dialog with Discourse Patterns and Knowledge Grounding

no code implementations EMNLP 2020 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

In this work, we perform the first large-scale analysis of discourse in media dialog and its impact on generative modeling of dialog turns, with a focus on interrogative patterns and use of external knowledge.

Large Language Models as Zero-Shot Conversational Recommenders

1 code implementation19 Aug 2023 Zhankui He, Zhouhang Xie, Rahul Jha, Harald Steck, Dawen Liang, Yesu Feng, Bodhisattwa Prasad Majumder, Nathan Kallus, Julian McAuley

In this paper, we present empirical studies on conversational recommendation tasks using representative large language models in a zero-shot setting with three primary contributions.

KNOW How to Make Up Your Mind! Adversarially Detecting and Alleviating Inconsistencies in Natural Language Explanations

no code implementations5 Jun 2023 Myeongjun Jang, Bodhisattwa Prasad Majumder, Julian McAuley, Thomas Lukasiewicz, Oana-Maria Camburu

While recent works have been considerably improving the quality of the natural language explanations (NLEs) generated by a model to justify its predictions, there is very limited research in detecting and alleviating inconsistencies among generated NLEs.

Adversarial Attack

Aligning Language Models to User Opinions

no code implementations24 May 2023 EunJeong Hwang, Bodhisattwa Prasad Majumder, Niket Tandon

An important aspect of developing LLMs that interact with humans is to align models' behavior to their users.

Open-Ended Question Answering

InterFair: Debiasing with Natural Language Feedback for Fair Interpretable Predictions

no code implementations14 Oct 2022 Bodhisattwa Prasad Majumder, Zexue He, Julian McAuley

Debiasing methods in NLP models traditionally focus on isolating information related to a sensitive attribute (like gender or race).

Controlling Bias Exposure for Fair Interpretable Predictions

no code implementations14 Oct 2022 Zexue He, Yu Wang, Julian McAuley, Bodhisattwa Prasad Majumder

However, when sensitive information is semantically entangled with the task information of the input, e. g., gender information is predictive for a profession, a fair trade-off between task performance and bias mitigation is difficult to achieve.

text-classification Text Classification

On Faithfulness and Coherence of Language Explanations for Recommendation Systems

no code implementations12 Sep 2022 Zhouhang Xie, Julian McAuley, Bodhisattwa Prasad Majumder

Reviews contain rich information about product characteristics and user interests and thus are commonly used to boost recommender system performance.

Recommendation Systems Review Generation

Achieving Conversational Goals with Unsupervised Post-hoc Knowledge Injection

1 code implementation ACL 2022 Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley

In this paper, we propose a post-hoc knowledge-injection technique where we first retrieve a diverse set of relevant knowledge snippets conditioned on both the dialog history and an initial response from an existing dialog model.

Informativeness Specificity

Self-Supervised Bot Play for Conversational Recommendation with Justifications

no code implementations9 Dec 2021 Shuyang Li, Bodhisattwa Prasad Majumder, Julian McAuley

Conversational recommender systems offer the promise of interactive, engaging ways for users to find items they enjoy.

Recommendation Systems

Knowledge-Grounded Self-Rationalization via Extractive and Natural Language Explanations

no code implementations25 Jun 2021 Bodhisattwa Prasad Majumder, Oana-Maria Camburu, Thomas Lukasiewicz, Julian McAuley

Our framework improves over previous methods by: (i) reaching SOTA task performance while also providing explanations, (ii) providing two types of explanations, while existing models usually provide only one type, and (iii) beating by a large margin the previous SOTA in terms of quality of both types of explanations.

Decision Making

Unsupervised Enrichment of Persona-grounded Dialog with Background Stories

1 code implementation ACL 2021 Bodhisattwa Prasad Majumder, Taylor Berg-Kirkpatrick, Julian McAuley, Harsh Jhamtani

Humans often refer to personal narratives, life experiences, and events to make a conversation more engaging and rich.

Ask what's missing and what's useful: Improving Clarification Question Generation using Global Knowledge

1 code implementation NAACL 2021 Bodhisattwa Prasad Majumder, Sudha Rao, Michel Galley, Julian McAuley

The ability to generate clarification questions i. e., questions that identify useful missing information in a given context, is important in reducing ambiguity.

Question Generation Question-Generation

Like hiking? You probably enjoy nature: Persona-grounded Dialog with Commonsense Expansions

1 code implementation EMNLP 2020 Bodhisattwa Prasad Majumder, Harsh Jhamtani, Taylor Berg-Kirkpatrick, Julian McAuley

Existing persona-grounded dialog models often fail to capture simple implications of given persona descriptions, something which humans are able to do seamlessly.

Interview: A Large-Scale Open-Source Corpus of Media Dialog

no code implementations7 Apr 2020 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

Compared to existing large-scale proxies for conversational data, language models trained on our dataset exhibit better zero-shot out-of-domain performance on existing spoken dialog datasets, demonstrating its usefulness in modeling real-world conversations.

ReZero is All You Need: Fast Convergence at Large Depth

13 code implementations10 Mar 2020 Thomas Bachlechner, Bodhisattwa Prasad Majumder, Huanru Henry Mao, Garrison W. Cottrell, Julian McAuley

Deep networks often suffer from vanishing or exploding gradients due to inefficient signal propagation, leading to long training times or convergence difficulties.

Language Modelling

Generating Personalized Recipes from Historical User Preferences

1 code implementation IJCNLP 2019 Bodhisattwa Prasad Majumder, Shuyang Li, Jianmo Ni, Julian McAuley

Existing approaches to recipe generation are unable to create recipes for users with culinary preferences but incomplete knowledge of ingredients in specific dishes.

Recipe Generation Text Generation

Deep Recurrent Neural Networks for Product Attribute Extraction in eCommerce

no code implementations29 Mar 2018 Bodhisattwa Prasad Majumder, Aditya Subramanian, Abhinandan Krishnan, Shreyansh Gandhi, Ajinkya More

Extracting accurate attribute qualities from product titles is a vital component in delivering eCommerce customers with a rewarding online shopping experience via an enriched faceted search.

Attribute Extraction

Fault Detection Engine in Intelligent Predictive Analytics Platform for DCIM

1 code implementation16 Oct 2016 Bodhisattwa Prasad Majumder, Ayan Sengupta, Sajal jain, Parikshit Bhaduri

With the advancement of huge data generation and data handling capability, Machine Learning and Probabilistic modelling enables an immense opportunity to employ predictive analytics platform in high security critical industries namely data centers, electricity grids, utilities, airport etc.

Community Detection Fault Detection

Kernelized Weighted SUSAN based Fuzzy C-Means Clustering for Noisy Image Segmentation

1 code implementation28 Mar 2016 Satrajit Mukherjee, Bodhisattwa Prasad Majumder, Aritran Piplai, Swagatam Das

The paper proposes a novel Kernelized image segmentation scheme for noisy images that utilizes the concept of Smallest Univalue Segment Assimilating Nucleus (SUSAN) and incorporates spatial constraints by computing circular colour map induced weights.

Clustering Image Segmentation +1

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