Zhe Feng


Weakly Supervised Named Entity Tagging with Learnable Logical Rules
Jiacheng Li | Haibo Ding | Jingbo Shang | Julian McAuley | Zhe Feng
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 building entity tagging systems by using a few rules as weak supervision. Previous methods mostly focus on disambiguating entity types based on contexts and expert-provided rules, while assuming entity spans are given. In this work, we propose a novel method TALLOR that bootstraps high-quality logical rules to train a neural tagger in a fully automated manner. Specifically, we introduce compound rules that are composed from simple rules to increase the precision of boundary detection and generate more diverse pseudo labels. We further design a dynamic label selection strategy to ensure pseudo label quality and therefore avoid overfitting the neural tagger. Experiments on three datasets demonstrate that our method outperforms other weakly supervised methods and even rivals a state-of-the-art distantly supervised tagger with a lexicon of over 2,000 terms when starting from only 20 simple rules. Our method can serve as a tool for rapidly building taggers in emerging domains and tasks. Case studies show that learned rules can potentially explain the predicted entities.

GLaRA: Graph-based Labeling Rule Augmentation for Weakly Supervised Named Entity Recognition
Xinyan Zhao | Haibo Ding | Zhe Feng
Proceedings of the 16th Conference of the European Chapter of the Association for Computational Linguistics: Main Volume

Instead of using expensive manual annotations, researchers have proposed to train named entity recognition (NER) systems using heuristic labeling rules. However, devising labeling rules is challenging because it often requires a considerable amount of manual effort and domain expertise. To alleviate this problem, we propose GLARA, a graph-based labeling rule augmentation framework, to learn new labeling rules from unlabeled data. We first create a graph with nodes representing candidate rules extracted from unlabeled data. Then, we design a new graph neural network to augment labeling rules by exploring the semantic relations between rules. We finally apply the augmented rules on unlabeled data to generate weak labels and train a NER model using the weakly labeled data. We evaluate our method on three NER datasets and find that we can achieve an average improvement of +20% F1 score over the best baseline when given a small set of seed rules.

Modeling Endorsement for Multi-Document Abstractive Summarization
Logan Lebanoff | Bingqing Wang | Zhe Feng | Fei Liu
Proceedings of the Third Workshop on New Frontiers in Summarization

A crucial difference between single- and multi-document summarization is how salient content manifests itself in the document(s). While such content may appear at the beginning of a single document, essential information is frequently reiterated in a set of documents related to a particular topic, resulting in an endorsement effect that increases information salience. In this paper, we model the cross-document endorsement effect and its utilization in multiple document summarization. Our method generates a synopsis from each document, which serves as an endorser to identify salient content from other documents. Strongly endorsed text segments are used to enrich a neural encoder-decoder model to consolidate them into an abstractive summary. The method has a great potential to learn from fewer examples to identify salient content, which alleviates the need for costly retraining when the set of documents is dynamically adjusted. Through extensive experiments on benchmark multi-document summarization datasets, we demonstrate the effectiveness of our proposed method over strong published baselines. Finally, we shed light on future research directions and discuss broader challenges of this task using a case study.

A New Approach to Overgenerating and Scoring Abstractive Summaries
Kaiqiang Song | Bingqing Wang | Zhe Feng | Fei Liu
Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies

We propose a new approach to generate multiple variants of the target summary with diverse content and varying lengths, then score and select admissible ones according to users’ needs. Abstractive summarizers trained on single reference summaries may struggle to produce outputs that achieve multiple desirable properties, i.e., capturing the most important information, being faithful to the original, grammatical and fluent. In this paper, we propose a two-staged strategy to generate a diverse set of candidate summaries from the source text in stage one, then score and select admissible ones in stage two. Importantly, our generator gives a precise control over the length of the summary, which is especially well-suited when space is limited. Our selectors are designed to predict the optimal summary length and put special emphasis on faithfulness to the original text. Both stages can be effectively trained, optimized and evaluated. Our experiments on benchmark summarization datasets suggest that this paradigm can achieve state-of-the-art performance.


Learning to Classify Events from Human Needs Category Descriptions
Haibo Ding | Zhe Feng
Findings of the Association for Computational Linguistics: EMNLP 2020

We study the problem of learning an event classifier from human needs category descriptions, which is challenging due to: (1) the use of highly abstract concepts in natural language descriptions, (2) the difficulty of choosing key concepts. To tackle these two challenges, we propose LeaPI, a zero-shot learning method that first automatically generate weak labels by instantiating high-level concepts with prototypical instances and then trains a human needs classifier with the weakly labeled data. To filter noisy concepts, we design a reinforced selection algorithm to choose high-quality concepts for instantiation. Experimental results on the human needs categorization task show that our method outperforms baseline methods, producing substantially better precision.


Improving Human Needs Categorization of Events with Semantic Classification
Haibo Ding | Ellen Riloff | Zhe Feng
Proceedings of the Eighth Joint Conference on Lexical and Computational Semantics (*SEM 2019)

Human Needs categories have been used to characterize the reason why an affective event is positive or negative. For example, “I got the flu” and “I got fired” are both negative (undesirable) events, but getting the flu is a Health problem while getting fired is a Financial problem. Previous work created learning models to assign events to Human Needs categories based on their words and contexts. In this paper, we introduce an intermediate step that assigns words to relevant semantic concepts. We create lightly supervised models that learn to label words with respect to 10 semantic concepts associated with Human Needs categories, and incorporate these labels as features for event categorization. Our results show that recognizing relevant semantic concepts improves both the recall and precision of Human Needs categorization for events.


Improving Slot Filling in Spoken Language Understanding with Joint Pointer and Attention
Lin Zhao | Zhe Feng
Proceedings of the 56th Annual Meeting of the Association for Computational Linguistics (Volume 2: Short Papers)

We present a generative neural network model for slot filling based on a sequence-to-sequence (Seq2Seq) model together with a pointer network, in the situation where only sentence-level slot annotations are available in the spoken dialogue data. This model predicts slot values by jointly learning to copy a word which may be out-of-vocabulary (OOV) from an input utterance through a pointer network, or generate a word within the vocabulary through an attentional Seq2Seq model. Experimental results show the effectiveness of our slot filling model, especially at addressing the OOV problem. Additionally, we integrate the proposed model into a spoken language understanding system and achieve the state-of-the-art performance on the benchmark data.


CHAT to Your Destination
Fuliang Weng | Baoshi Yan | Zhe Feng | Florin Ratiu | Madhuri Raya | Brian Lathrop | Annie Lien | Sebastian Varges | Rohit Mishra | Feng Lin | Matthew Purver | Harry Bratt | Yao Meng | Stanley Peters | Tobias Scheideck | Badri Raghunathan | Zhaoxia Zhang
Proceedings of the 8th SIGdial Workshop on Discourse and Dialogue

A Conversational In-Car Dialog System
Baoshi Yan | Fuliang Weng | Zhe Feng | Florin Ratiu | Madhuri Raya | Yao Meng | Sebastian Varges | Matthew Purver | Annie Lien | Tobias Scheideck | Badri Raghunathan | Feng Lin | Rohit Mishra | Brian Lathrop | Zhaoxia Zhang | Harry Bratt | Stanley Peters
Proceedings of Human Language Technologies: The Annual Conference of the North American Chapter of the Association for Computational Linguistics (NAACL-HLT)


A Progressive Feature Selection Algorithm for Ultra Large Feature Spaces
Qi Zhang | Fuliang Weng | Zhe Feng
Proceedings of the 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics