Le Song


2022

pdf bib
Prompt-Based Rule Discovery and Boosting for Interactive Weakly-Supervised Learning
Rongzhi Zhang | Yue Yu | Pranav Shetty | Le Song | Chao Zhang
Proceedings of the 60th Annual Meeting of the Association for Computational Linguistics (Volume 1: Long Papers)

Weakly-supervised learning (WSL) has shown promising results in addressing label scarcity on many NLP tasks, but manually designing a comprehensive, high-quality labeling rule set is tedious and difficult. We study interactive weakly-supervised learning—the problem of iteratively and automatically discovering novel labeling rules from data to improve the WSL model. Our proposed model, named PRBoost, achieves this goal via iterative prompt-based rule discovery and model boosting. It uses boosting to identify large-error instances and discovers candidate rules from them by prompting pre-trained LMs with rule templates. The candidate rules are judged by human experts, and the accepted rules are used to generate complementary weak labels and strengthen the current model. Experiments on four tasks show PRBoost outperforms state-of-the-art WSL baselines up to 7.1%, and bridges the gaps with fully supervised models.

2021

pdf bib
BERTifying the Hidden Markov Model for Multi-Source Weakly Supervised Named Entity Recognition
Yinghao Li | Pranav Shetty | Lucas Liu | Chao Zhang | Le Song
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)

We study the problem of learning a named entity recognition (NER) tagger using noisy labels from multiple weak supervision sources. Though cheap to obtain, the labels from weak supervision sources are often incomplete, inaccurate, and contradictory, making it difficult to learn an accurate NER model. To address this challenge, we propose a conditional hidden Markov model (CHMM), which can effectively infer true labels from multi-source noisy labels in an unsupervised way. CHMM enhances the classic hidden Markov model with the contextual representation power of pre-trained language models. Specifically, CHMM learns token-wise transition and emission probabilities from the BERT embeddings of the input tokens to infer the latent true labels from noisy observations. We further refine CHMM with an alternate-training approach (CHMM-ALT). It fine-tunes a BERT-NER model with the labels inferred by CHMM, and this BERT-NER’s output is regarded as an additional weak source to train the CHMM in return. Experiments on four NER benchmarks from various domains show that our method outperforms state-of-the-art weakly supervised NER models by wide margins.

2020

pdf bib
Question Directed Graph Attention Network for Numerical Reasoning over Text
Kunlong Chen | Weidi Xu | Xingyi Cheng | Zou Xiaochuan | Yuyu Zhang | Le Song | Taifeng Wang | Yuan Qi | Wei Chu
Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP)

Numerical reasoning over texts, such as addition, subtraction, sorting and counting, is a challenging machine reading comprehension task, since it requires both natural language understanding and arithmetic computation. To address this challenge, we propose a heterogeneous graph representation for the context of the passage and question needed for such reasoning, and design a question directed graph attention network to drive multi-step numerical reasoning over this context graph. Our model, which combines deep learning and graph reasoning, achieves remarkable results in benchmark datasets such as DROP.

2019

pdf bib
Language Modeling with Shared Grammar
Yuyu Zhang | Le Song
Proceedings of the 57th Annual Meeting of the Association for Computational Linguistics

Sequential recurrent neural networks have achieved superior performance on language modeling, but overlook the structure information in natural language. Recent works on structure-aware models have shown promising results on language modeling. However, how to incorporate structure knowledge on corpus without syntactic annotations remains an open problem. In this work, we propose neural variational language model (NVLM), which enables the sharing of grammar knowledge among different corpora. Experimental results demonstrate the effectiveness of our framework on two popular benchmark datasets. With the help of shared grammar, our language model converges significantly faster to a lower perplexity on new training corpus.