Dan Klein

Long-Range Understanding and Consistency in Story Generation

This is a continuation of our previous Year 4 collaboration, Coherent and Consistent Long Story Generation.

Participants

Berkeley Advisor: Dan Klein, klein@berkeley.edu...

Coherent and Consistent Long Story Generation

This is a continuation of our previous Year 3 collaboration, Learning-Driven Exploration For Search

Participants

Berkeley Advisor: Dan Klein, klein@berkeley.edu...

Enabling Non-Experts to Annotate Complex Logical Forms at Scale

The goal of semantic parsing is to map natural language utterances into logical forms, which will then be executed to fulfill the users’ needs. For example, a user might seek information by asking “What’s the height of the highest mountain in the U.S.”, and the semantic parser will produce an SQL query Select Max(altitude) from Mountain where country = ‘U.S.’, and execute it against a database to produce the answer. Semantic parsers can also be used to formally represent intended actions, track dialogue states, or process...

Pre-trained Representations for Language-Guided Web Navigation

Overview

Personal assistants that interact with open-domain websites can assist humans with arbitrary tasks, such as booking flights or searching for information. Language-guided assistants can be a natural interface for users, enabling assistive technologies, browser automation tools, and web navigation in situations where people cannot use standard interfaces (e.g., while cooking or while browsing a website in an unfamiliar language).

Most existing web navigation assistants rely on text-only pretrained representations, which do not take advantage of structural information in web...

Learning-Driven Exploration For Search

Kevin Yang, Tianjun Zhang, Yuandong Tian, Dan Klein

Summary

Search/path planning is an important topic with applications to a variety of other domains, e.g., RL, robotics, chemistry, compiler optimization. However, random search is too inefficient for many applications. Classic global approaches such as Bayesian Optimization may struggle with high-dimensional spaces and smaller numbers of samples. Local approaches such as CEM and CMA-ES may struggle to escape local optima. We propose to explore and partition the search...