Papers with Mandarin
Extracting Lexical Features from Dialects via Interpretable Dialect Classifiers (2024.naacl-short)
Copied to clipboard
| Challenge: | Identifying linguistic differences between dialects of a language often requires expert knowledge and meticulous human analysis. |
| Approach: | They propose a method to extract distinguishing lexical features of dialects by utilizing interpretable dialect classifiers in the absence of human experts. |
| Outcome: | The proposed method extracts key language-specific lexical features that contribute to dialectal variations. |
Token Sequence Labeling vs. Clause Classification for English Emotion Stimulus Detection (2020.starsem-1)
Copied to clipboard
| Challenge: | Emotion stimulus detection is the task of finding the cause of an emotion in a textual description. |
| Approach: | They propose to evaluate whether clause classification or token sequence labeling is better for emotion stimulus detection in English. |
| Outcome: | The proposed framework compares clause classification and token sequence labeling on four English datasets. |
Encoding of lexical tone in self-supervised models of spoken language (2024.naacl-long)
Copied to clipboard
| Challenge: | Existing research on representations of phonetic and phonological information has focused on segmental features such as phonemes. |
| Approach: | They propose to analyze the tone encoding capabilities of self-supervised Spoken Language Models, using Mandarin and Vietnamese as case studies. |
| Outcome: | The proposed models encode lexical tone even when trained on non-tonal languages. |
ChildTalk: A Multi-Dialect Chinese Child Speech Corpus with Full-Length Child–Caregiver Conversations for Speech Recognition (2026.findings-acl)
Copied to clipboard
| Challenge: | Automatic speech recognition (ASR) for children remains challenging due to developmental variability and the scarcity of high-quality corpora. |
| Approach: | They propose a large-scale Chinese child speech corpus that contains 112.5 hours of speech from 498 children and 500 caregivers. |
| Outcome: | The proposed model improves in-domain and cross-domain performance on children's speech. |
Speech-to-Speech Translation for a Real-world Unwritten Language (2023.findings-acl)
Copied to clipboard
Peng-Jen Chen, Kevin Tran, Yilin Yang, Jingfei Du, Justine Kao, Yu-An Chung, Paden Tomasello, Paul-Ambroise Duquenne, Holger Schwenk, Hongyu Gong, Hirofumi Inaguma, Sravya Popuri, Changhan Wang, Juan Pino, Wei-Ning Hsu, Ann Lee
| Challenge: | a new study examines speech-to-speech translation (S2ST) that translates speech from one language into another . the research area for unwritten languages remains a research area with little exploration due to the lack of training data. |
| Approach: | They propose a system that translates speech from one language into another . they use Taiwanese Hokkien as an example of an unwritten language . |
| Outcome: | The proposed system can be used to train models in languages without standard writing systems. |
WeCanTalk: A New Multi-language, Multi-modal Resource for Speaker Recognition (2022.lrec-1)
Copied to clipboard
| Challenge: | The WeCanTalk corpus is a multi-modal, multi-language resource for speaker recognition. |
| Approach: | The WeCanTalk corpus is a multi-modal resource for speaker recognition. |
| Outcome: | The corpus contains data from 202 native speakers in Hong Kong who were fluent in at least one other language. |
How Do Neural Sequence Models Generalize? Local and Global Cues for Out-of-Distribution Prediction (2021.emnlp-main)
Copied to clipboard
| Challenge: | Using RNN and transformer language models, we show consistent generalization in out-of-distribution contexts. |
| Approach: | They propose two idealized models of generalization in next-word prediction . they show that neural language models interpolate between these two forms of generalisation . |
| Outcome: | The proposed models exhibit consistent generalization in out-of-distribution contexts. |
Multi3Hate: Multimodal, Multilingual, and Multicultural Hate Speech Detection with Vision–Language Models (2025.naacl-long)
Copied to clipboard
| Challenge: | a new study shows that cultural background significantly affects multimodal hate speech moderation models . a limited dataset excludes multi-modal forms of hate and excludes non-English-speaking cultures . the lowest pairwise label agreement between the USA and India is due to cultural factors . |
| Approach: | They use a multimodal and multilingual parallel hate speech dataset to examine cultural differences . they find that cultural background significantly affects multimodal hate speech annotation . |
| Outcome: | The proposed dataset shows that cultural background significantly affects multimodal hate speech annotation. |
Systematic Analysis of Image Schemas in Natural Language through Explainable Multilingual Neural Language Processing (2022.coling-1)
Copied to clipboard
| Challenge: | Existing methods for automatic detection of image schemas in natural language rely on specific assumptions about word classes as indicators of spatio-temporal events. |
| Approach: | They propose to train a supervised classifier that classifies natural language expressions into image schemas using a large dataset of examples from image schema literature. |
| Outcome: | The proposed model performs best in German, Russian, and French, and is based on a small dataset of examples from image schema literature. |