Search Results for author: Nikhita Vedula

Found 9 papers, 2 papers with code

Question Suggestion for Conversational Shopping Assistants Using Product Metadata

no code implementations2 May 2024 Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi

Digital assistants have become ubiquitous in e-commerce applications, following the recent advancements in Information Retrieval (IR), Natural Language Processing (NLP) and Generative Artificial Intelligence (AI).

Friction In-Context Learning +2

Leveraging Interesting Facts to Enhance User Engagement with Conversational Interfaces

1 code implementation9 Apr 2024 Nikhita Vedula, Giuseppe Castellucci, Eugene Agichtein, Oleg Rokhlenko, Shervin Malmasi

Conversational Task Assistants (CTAs) guide users in performing a multitude of activities, such as making recipes.

Instant Answering in E-Commerce Buyer-Seller Messaging using Message-to-Question Reformulation

no code implementations18 Jan 2024 Besnik Fetahu, Tejas Mehta, Qun Song, Nikhita Vedula, Oleg Rokhlenko, Shervin Malmasi

E-commerce customers frequently seek detailed product information for purchase decisions, commonly contacting sellers directly with extended queries.

Question Answering

Faithful Low-Resource Data-to-Text Generation through Cycle Training

1 code implementation24 May 2023 Zhuoer Wang, Marcus Collins, Nikhita Vedula, Simone Filice, Shervin Malmasi, Oleg Rokhlenko

Cycle training uses two models which are inverses of each other: one that generates text from structured data, and one which generates the structured data from natural language text.

Data-to-Text Generation

Automatic Discovery of Novel Intents & Domains from Text Utterances

no code implementations22 May 2020 Nikhita Vedula, Rahul Gupta, Aman Alok, Mukund Sridhar

We propose a novel framework, ADVIN, to automatically discover novel domains and intents from large volumes of unlabeled data.

General Classification Natural Language Understanding +1

Towards Open Intent Discovery for Conversational Text

no code implementations17 Apr 2019 Nikhita Vedula, Nedim Lipka, Pranav Maneriker, Srinivasan Parthasarathy

Existing research for intent discovery model it as a classification task with a predefined set of known categories.

Intent Discovery Open Intent Discovery

Multimodal Content Analysis for Effective Advertisements on YouTube

no code implementations12 Sep 2017 Nikhita Vedula, Wei Sun, Hyunhwan Lee, Harsh Gupta, Mitsunori Ogihara, Joseph Johnson, Gang Ren, Srinivasan Parthasarathy

The objective of this work is then to measure the effectiveness of an advertisement, and to recommend a useful set of features to advertisement designers to make it more successful and approachable to users.

Recommendation Systems

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