# Shallow Discourse Annotation for Chinese TED Talks

arXiv: https://arxiv.org/abs/2003.04032 · HTML: https://mlsanity.com/paper/2003.04032/

## Key facts

- Cited by 13 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 9 Mar 2020.

## Built on by (most cited first, 13 of 13)

- [Consistency is Key: Disentangling Label Variation in Natural Language Processing with Intra-Annotator Agreement](https://mlsanity.com/paper/2301.10684/) (2023)
- [Probing Multilingual Language Models for Discourse](https://mlsanity.com/paper/2106.04832/) (2021)
- [The Box is in the Pen: Evaluating Commonsense Reasoning in Neural Machine Translation](https://mlsanity.com/paper/2503.03308/) (2025)
- [Instances and Labels: Hierarchy-aware Joint Supervised Contrastive Learning for Hierarchical Multi-Label Text Classification](https://mlsanity.com/paper/2310.05128/) (2023)
- [BUCA: A Binary Classification Approach to Unsupervised Commonsense Question Answering](https://mlsanity.com/paper/2305.15932/) (2023)
- [Tgea: An error-annotated dataset and benchmark tasks for text generation from pretrained language models](https://mlsanity.com/paper/2503.04232/) (2025)
- [Fine-Grained Evaluation for Implicit Discourse Relation Recognition](https://mlsanity.com/paper/2503.05326/) (2025)
- [Evaluating Discourse Cohesion in Pre-trained Language Models](https://mlsanity.com/paper/2503.06137/) (2025)
- [Meta-RTL: Reinforcement-Based Meta-Transfer Learning for Low-Resource Commonsense Reasoning](https://mlsanity.com/paper/2409.19075/) (2024)
- [Similarity-Based Domain Adaptation with LLMs](https://mlsanity.com/paper/2503.05281/) (2025)
- [MetaXCR: Reinforcement-Based Meta-Transfer Learning for Cross-Lingual Commonsense Reasoning](https://mlsanity.com/paper/2503.06531/) (2025)
- [Joint Training And Decoding for Multilingual End-to-End Simultaneous Speech Translation](https://mlsanity.com/paper/2503.11080/) (2025)
- [AdaST: Dynamically Adapting Encoder States in the Decoder for End-to-End Speech-to-Text Translation](https://mlsanity.com/paper/2503.14185/) (2025)

## Abstract (arXiv)

Text corpora annotated with language-related properties are an important resource for the development of Language Technology. The current work contributes a new resource for Chinese Language Technology and for Chinese-English translation, in the form of a set of TED talks (some originally given in English, some in Chinese) that have been annotated with discourse relations in the style of the Penn Discourse TreeBank, adapted to properties of Chinese text that are not present in English. The resource is currently unique in annotating discourse-level properties of planned spoken monologues rather than of written text. An inter-annotator agreement study demonstrates that the annotation scheme is able to achieve highly reliable results.