LOT: A Story-Centric Benchmark for Evaluating Chinese Long Text Understanding and Generation
Published
2022-04-11
Jian Guan
,
Zhuoer Feng
,
Yamei Chen
,
Ruilin He
,
Xiaoxi Mao
,
Changjie Fan
,
Minlie Huang
Jian Guan
Department of computer science and technology, Tsinghua University, Beijing, China
Zhuoer Feng
Department of computer science and technology, Tsinghua University, Beijing, China
Yamei Chen
Department of computer science and technology, Tsinghua University, Beijing, China
Ruilin He
Huawei Technologies Co., Ltd
Xiaoxi Mao
Netease Fuxi AI Lab
Changjie Fan
Netease Fuxi AI Lab
Minlie Huang
Department of computer science and technology, Tsinghua University, Beijing, China
Abstract
Standard multi-task benchmarks are essential for developing pretraining models that can generalize to various downstream tasks. Existing benchmarks for natural language processing (NLP) usually focus only on understanding or generating short texts. However, long text modeling requires many distinct abilities in contrast to short texts, such as the modeling of long-range discourse and commonsense relations, and the coherence and controllability of generation. The lack of standardized benchmarks makes it difficult to assess these abilities of a model and fairly compare different models, especially Chinese models. Therefore, we propose a story-centric benchmark named LOT for evaluating Chinese long text modeling, which aggregates two understanding tasks and two generation tasks. We construct new datasets for these tasks based on human-written Chinese stories with hundreds of words. Furthermore, we release an encoder-decoder-based Chinese long text pretraining model named LongLM with up to 1 billion parameters. We pretrain LongLM on 120G Chinese novels with two generative tasks including text infilling and conditional continuation. Extensive experiments show that LongLM outperforms similar-sized pretraining models substantially on both the understanding and generation tasks in LOT.
Presented at ACL 2022
Article at MIT Press