Tenghao Huang


2022

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Read Top News First: A Document Reordering Approach for Multi-Document News Summarization
Chao Zhao | Tenghao Huang | Somnath Basu Roy Chowdhury | Muthu Kumar Chandrasekaran | Kathleen McKeown | Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: ACL 2022

A common method for extractive multi-document news summarization is to re-formulate it as a single-document summarization problem by concatenating all documents as a single meta-document. However, this method neglects the relative importance of documents. We propose a simple approach to reorder the documents according to their relative importance before concatenating and summarizing them. The reordering makes the salient content easier to learn by the summarization model. Experiments show that our approach outperforms previous state-of-the-art methods with more complex architectures.

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Revisiting Generative Commonsense Reasoning: A Pre-Ordering Approach
Chao Zhao | Faeze Brahman | Tenghao Huang | Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: NAACL 2022

Pre-trained models (PTMs) have lead to great improvements in natural language generation (NLG). However, it is still unclear how much commonsense knowledge they possess. With the goal of evaluating commonsense knowledge of NLG models, recent work has proposed the problem of generative commonsense reasoning, e.g., to compose a logical sentence given a set of unordered concepts. Existing approaches to this problem hypothesize that PTMs lack sufficient parametric knowledge for this task, which can be overcome by introducing external knowledge or task-specific pre-training objectives. Different from this trend, we argue that PTM’s inherent ability for generative commonsense reasoning is underestimated due to the order-agnostic property of its input. In particular, we hypothesize that the order of the input concepts can affect the PTM’s ability to utilize its commonsense knowledge. To this end, we propose a pre-ordering approach to elaborately manipulate the order of the given concepts before generation. Experiments show that our approach can outperform the more sophisticated models that have access to a lot of external data and resources.

2021

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Uncovering Implicit Gender Bias in Narratives through Commonsense Inference
Tenghao Huang | Faeze Brahman | Vered Shwartz | Snigdha Chaturvedi
Findings of the Association for Computational Linguistics: EMNLP 2021

Pre-trained language models learn socially harmful biases from their training corpora, and may repeat these biases when used for generation. We study gender biases associated with the protagonist in model-generated stories. Such biases may be expressed either explicitly (“women can’t park”) or implicitly (e.g. an unsolicited male character guides her into a parking space). We focus on implicit biases, and use a commonsense reasoning engine to uncover them. Specifically, we infer and analyze the protagonist’s motivations, attributes, mental states, and implications on others. Our findings regarding implicit biases are in line with prior work that studied explicit biases, for example showing that female characters’ portrayal is centered around appearance, while male figures’ focus on intellect.