paper-with-me

홈 › Papers

Automatic Inference of the Tense of Chinese Events Using Implicit Linguistic Information

2014-10-01 · EMNLP 2014 10 · Yuchen Zhang, Nianwen Xue
📄 PDF Abstract BibTeX

Code (0)

등록된 구현이 없습니다.

Tasks

Feature EngineeringMachine Translation

Similar Papers 제목 키워드 기반

Chinese Tense Labelling and Causal Analysis

2016-12-01 · COLING 2016 12 · Hen-Hsen Huang, Chang-Rui Yang, Hsin-Hsi Chen

This paper explores the role of tense information in Chinese causal analysis. Both tasks of causal type classification and causal directionality identification are experimented to show the significant improvement gained …

General ClassificationQuestion Answering

Buy one get one free: Distant annotation of Chinese tense, event type and modality

2014-05-01 · LREC 2014 5 · Nianwen Xue, Yuchen Zhang

We describe a {``}distant annotation{''} method where we mark up the semantic tense, event type, and modality of Chinese events via a word-aligned parallel corpus. We first map Chinese verbs to their English counterparts…

Machine TranslationVocal Bursts Type PredictionWord Alignment

LLMs Struggle with NLI for Perfect Aspect: A Cross-Linguistic Study in Chinese and Japanese

2025-08-16 · Jie Lu, Du Jin, Hitomi Yanaka arxiv

Unlike English, which uses distinct forms (e.g., had, has, will have) to mark the perfect aspect across tenses, Chinese and Japanese lack separate grammatical forms for tense within the perfect aspect, which complicates …

Natural Language Inference

Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense

2021-04-01 · EACL 2021 2 · Bingzhi Li, Guillaume Wisniewski

We evaluate the ability of Bert embeddings to represent tense information, taking French and Chinese as a case study. In French, the tense information is expressed by verb morphology and can be captured by simple surface…

Sentence

One Tense per Scene: Predicting Tense in Chinese Conversations

2015-07-01 · IJCNLP 2015 7 · Tao Ge, Heng Ji, Baobao Chang, Zhifang Sui
Machine Translation