activity
20192025
most citedMMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

13 citations · 18 across the 5 of their papers we have counts for

collaborators

5 papers

cs.AI20252 cited

The Amazon Nova Family of Models: Technical Report and Model Card

Amazon AGI, Aaron Langford, Aayush Shah +783

We present Amazon Nova, a new generation of state-of-the-art foundation models that deliver frontier intelligence and industry-leading price performance. Amazon Nova Pro is a highl…

cs.CL2024

Redefining Proactivity for Information Seeking Dialogue

Jing Yang Lee, Seokhwan Kim, Kartik Mehta +3

Information-Seeking Dialogue (ISD) agents aim to provide accurate responses to user queries. While proficient in directly addressing user queries, these agents, as well as LLMs in…

cs.CL2021

Style Control for Schema-Guided Natural Language Generation

Alicia Y. Tsai, Shereen Oraby, Vittorio Perera +5

Natural Language Generation (NLG) for task-oriented dialogue systems focuses on communicating specific content accurately, fluently, and coherently. While these attributes are cruc…

cs.CL20213 cited

Alexa Conversations: An Extensible Data-driven Approach for Building Task-oriented Dialogue Systems

Anish Acharya, Suranjit Adhikari, Sanchit Agarwal +28

Traditional goal-oriented dialogue systems rely on various components such as natural language understanding, dialogue state tracking, policy learning and response generation. Trai…

cs.CL201913 cited

MMM: Multi-stage Multi-task Learning for Multi-choice Reading Comprehension

Di Jin, Shuyang Gao, Jiun-Yu Kao +2

Machine Reading Comprehension (MRC) for question answering (QA), which aims to answer a question given the relevant context passages, is an important way to test the ability of int…