Miao Chen


2022

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Towards Table-to-Text Generation with Pretrained Language Model: A Table Structure Understanding and Text Deliberating Approach
Miao Chen | Xinjiang Lu | Tong Xu | Yanyan Li | Zhou Jingbo | Dejing Dou | Hui Xiong
Proceedings of the 2022 Conference on Empirical Methods in Natural Language Processing

Although remarkable progress on the neural table-to-text methods has been made, the generalization issues hinder the applicability of these models due to the limited source tables. Large-scale pretrained language models sound like a promising solution to tackle such issues. However, how to effectively bridge the gap between the structured table and the text input by fully leveraging table information to fuel the pretrained model is still not well explored. Besides, another challenge of integrating the deliberation mechanism into the text-to-text pretrained model for solving the table-to-text task remains seldom studied. In this paper, to implement the table-to-text generation with pretrained language model, we propose a table structure understanding and text deliberating approach, namely TASD. To be specific, we devise a three-layered multi-head attention network to realize the table-structureaware text generation model with the help of the pretrained language model. Furthermore, a multi-pass decoder framework is adopted to enhance the capability of polishing generated text for table descriptions. The empirical studies, as well as human evaluation, on two public datasets, validate that our approach can generate faithful and fluent descriptive texts for different types of tables.

2020

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Joint Learning with Pre-trained Transformer on Named Entity Recognition and Relation Extraction Tasks for Clinical Analytics
Miao Chen | Ganhui Lan | Fang Du | Victor Lobanov
Proceedings of the 3rd Clinical Natural Language Processing Workshop

In drug development, protocols define how clinical trials are conducted, and are therefore of paramount importance. They contain key patient-, investigator-, medication-, and study-related information, often elaborated in different sections in the protocol texts. Granular-level parsing on large quantity of existing protocols can accelerate clinical trial design and provide actionable insights into trial optimization. Here, we report our progresses in using deep learning NLP algorithms to enable automated protocol analytics. In particular, we combined a pre-trained BERT transformer model with joint-learning strategies to simultaneously identify clinically relevant entities (i.e. Named Entity Recognition) and extract the syntactic relations between these entities (i.e. Relation Extraction) from the eligibility criteria section in protocol texts. When comparing to standalone NER and RE models, our joint-learning strategy can effectively improve the performance of RE task while retaining similarly high NER performance, likely due to the synergy of optimizing toward both tasks’ objectives via shared parameters. The derived NLP model provides an end-to-end solution to convert unstructured protocol texts into structured data source, which will be embedded into a comprehensive clinical analytics workflow for downstream trial design missions such like patient population extraction, patient enrollment rate estimation, and protocol amendment prediction.

2012

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Using Ontology-based Approaches to Representing Speech Transcripts for Automated Speech Scoring
Miao Chen
Proceedings of the NAACL HLT 2012 Student Research Workshop

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Using an Ontology for Improved Automated Content Scoring of Spontaneous Non-Native Speech
Miao Chen | Klaus Zechner
Proceedings of the Seventh Workshop on Building Educational Applications Using NLP

2011

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Computing and Evaluating Syntactic Complexity Features for Automated Scoring of Spontaneous Non-Native Speech
Miao Chen | Klaus Zechner
Proceedings of the 49th Annual Meeting of the Association for Computational Linguistics: Human Language Technologies