activity
20162024
most citedA Syntactic Neural Model for General-Purpose Code Generation

111 citations · 156 across the 5 of their papers we have counts for

collaborators

5 papers

cs.CL2024★ 2 cited

Stereotype or Personalization? User Identity Biases Chatbot Recommendations

Anjali Kantharuban, Jeremiah Milbauer, Maarten Sap +2

While personalized recommendations are often desired by users, it can be difficult in practice to distinguish cases of bias from cases of personalization: we find that models gener…

cs.CL2024

CMU's IWSLT 2024 Simultaneous Speech Translation System

Xi Xu, Siqi Ouyang, Brian Yan +5

This paper describes CMU's submission to the IWSLT 2024 Simultaneous Speech Translation (SST) task for translating English speech to German text in a streaming manner. Our end-to-e…

cs.AI2023★ 29 cited

WebArena: A Realistic Web Environment for Building Autonomous Agents

Shuyan Zhou, Frank F. Xu, Hao Zhu +9

With advances in generative AI, there is now potential for autonomous agents to manage daily tasks via natural language commands. However, current agents are primarily created and…

cs.CL2017★ 111 cited

A Syntactic Neural Model for General-Purpose Code Generation

Pengcheng Yin, Graham Neubig

We consider the problem of parsing natural language descriptions into source code written in a general-purpose programming language like Python. Existing data-driven methods treat…

cs.LG2016★ 14 cited

Eve: A Gradient Based Optimization Method with Locally and Globally Adaptive Learning Rates

Hiroaki Hayashi, Jayanth Koushik, Graham Neubig

Adaptive gradient methods for stochastic optimization adjust the learning rate for each parameter locally. However, there is also a global learning rate which must be tuned in orde…