Bill Yuchen Lin

We propose a knowledge-aware reasoning framework for answering commonsense questions, which effectively utilizes external, structured commonsense knowledge graphs to perform explainable inferences. Expand

In this paper, we present our multi-channel neural architecture for recognizing emerging named entity in social media messages, which we applied in the Novel and Emerging Named Entity Recognition shared task at the EMNLP 2017 Workshop on Noisy User-generated Text. Expand

This paper aims to automatically extract the commonsense LOCATEDNEAR relation between physical objects from textual corpora. Expand

We present a lightweight yet effective approach, and evaluate it on two novel tasks: 1) mining cross-cultural differences of named entities and 2) finding similar terms for slang across languages. Expand

In this paper, we present a constrained text generation task, CommonGen associated with a benchmark dataset, to explicitly test machines for the ability of generative commonsense reasoning. Expand

We present a procedure that allows for the systematized probing of both PTLMs' inference abilities and robustness. Expand

In this paper, we empirically investigate effective methods for conveniently adapting an existing, well-trained neural NER model for a new domain. Expand

Deep neural models for relation extraction tend to be less reliable when perfectly labeled data is limited. Expand

We introduce an open-source web-based data annotation framework (AlpacaTag) for sequence tagging tasks such as named-entity recognition (NER) and sequence labeling with recommendations powered by active learning. Expand

In this paper, we introduce "entity triggers," an effective proxy of human explanations for facilitating label-efficient learning of NER models. Expand