43 citations · 91 across the 11 of their papers we have counts for
12 papers
Interpretable Neural Embeddings with Sparse Self-Representation
Minxue Xia, Hao Zhu
Interpretability benefits the theoretical understanding of representations. Existing word embeddings are generally dense representations. Hence, the meaning of latent dimensions is…
Hierarchical Prompting Assists Large Language Model on Web Navigation
Abishek Sridhar, Robert Lo, Frank F. Xu +2
Large language models (LLMs) struggle on processing complicated observations in interactive decision making tasks. To alleviate this issue, we propose a simple hierarchical prompti…
Don't Copy the Teacher: Data and Model Challenges in Embodied Dialogue
So Yeon Min, Hao Zhu, Ruslan Salakhutdinov +1
Embodied dialogue instruction following requires an agent to complete a complex sequence of tasks from a natural language exchange. The recent introduction of benchmarks (Padmakuma…
Dependency Induction Through the Lens of Visual Perception
Ruisi Su, Shruti Rijhwani, Hao Zhu +4
Most previous work on grammar induction focuses on learning phrasal or dependency structure purely from text. However, because the signal provided by text alone is limited, recentl…
Few-shot Language Coordination by Modeling Theory of Mind
Hao Zhu, Graham Neubig, Yonatan Bisk
Humans communicate with a large community by coordinating with different interlocutors within short conversations. This ability has been understudie…
The Return of Lexical Dependencies: Neural Lexicalized PCFGs
Hao Zhu, Yonatan Bisk, Graham Neubig
In this paper we demonstrate that . This contrasts to the most p…
FewRel 2.0: Towards More Challenging Few-Shot Relation Classification
Tianyu Gao, Xu Han, Hao Zhu +4
We present FewRel 2.0, a more challenging task to investigate two aspects of few-shot relation classification models: (1) Can they adapt to a new domain with only a handful of inst…
Quantifying Similarity between Relations with Fact Distribution
Weize Chen, Hao Zhu, Xu Han +2
We introduce a conceptually simple and effective method to quantify the similarity between relations in knowledge bases. Specifically, our approach is based on the divergence betwe…
Graph Neural Networks with Generated Parameters for Relation Extraction
Hao Zhu, Yankai Lin, Zhiyuan Liu +3
Recently, progress has been made towards improving relational reasoning in machine learning field. Among existing models, graph neural networks (GNNs) is one of the most effective…
Language Modeling with Sparse Product of Sememe Experts
Yihong Gu, Jun Yan, Hao Zhu +5
Most language modeling methods rely on large-scale data to statistically learn the sequential patterns of words. In this paper, we argue that words are atomic language units but no…
FewRel: A Large-Scale Supervised Few-Shot Relation Classification Dataset with State-of-the-Art Evaluation
Xu Han, Hao Zhu, Pengfei Yu +4
We present a Few-Shot Relation Classification Dataset (FewRel), consisting of 70, 000 sentences on 100 relations derived from Wikipedia and annotated by crowdworkers. The relation…
Incorporating Chinese Characters of Words for Lexical Sememe Prediction
Huiming Jin, Hao Zhu, Zhiyuan Liu +4
Sememes are minimum semantic units of concepts in human languages, such that each word sense is composed of one or multiple sememes. Words are usually manually annotated with their…