| Challenge: | Synesthesia refers to the description of perceptions in one sensory modality through concepts from other modalities. |
| Approach: | They propose a task called synesthesia detection to extract the sensory word of a sentence and predict the original and synesthetic sensory modalities of the corresponding sensory word. |
| Outcome: | The proposed model achieves state-of-the-art on the Chinese synesthesia dataset. |
Similar Papers
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
Copied to clipboard
| Challenge: | A wide variety of neural-network architectures have been proposed for the task of Chinese word segmentation. |
| Approach: | They propose a bidirectional LSTM model with standard deep learning techniques and best practices for the task of Chinese word segmentation. |
| Outcome: | The proposed model outperforms models based on standard deep learning techniques and best practices on Chinese word segmentation datasets. |
Language Models at the Syntax-Semantics Interface: A Case Study of the Long-Distance Binding of Chinese Reflexive Ziji (2025.coling-main)
Copied to clipboard
| Challenge: | Existing language models tend to rely heavily on sequential cues, but not always favoring the closest strings. |
| Approach: | They construct a dataset of 320 synthetic sentences and 360 natural sentences from the BCC corpus . they evaluate 21 language models against this dataset and compare their performance to native Mandarin speakers . |
| Outcome: | The proposed models do not replicate human-like judgments in Mandarin Chinese . the results show that existing models tend to rely heavily on sequential cues . |
Overestimation of Syntactic Representation in Neural Language Models (2020.acl-main)
Copied to clipboard
| Challenge: | Several testing methodologies have been developed to probe models’ syntactic representations. |
| Approach: | They propose a method to determine syntactic structure by training a model on strings generated according to a template and testing its ability to distinguish between similar ones with different syntax. |
| Outcome: | The proposed method reproduces positive results with two non-syntactic baseline language models: an n-gram model and an LSTM model trained on scrambled inputs. |
RRNorm: A Novel Framework for Chinese Disease Diagnoses Normalization via LLM-Driven Terminology Component Recognition and Reconstruction (2024.findings-acl)
Copied to clipboard
| Challenge: | Clinical Terminology Normalization (CTN) aims at finding standard terms from a given termbase for mentions extracted from clinical texts. |
| Approach: | They propose a method that leverages reasoning capability of large language models to recognize components of terms and automate decomposition. |
| Outcome: | The proposed strategy achieves state-of-the-art on the experimental dataset. |
Neural Chinese Address Parsing (N19-1)
Copied to clipboard
| Challenge: | Recent research shows that systems that perform address parsing can be useful for building e-commerce or product recommendation systems. |
| Approach: | They propose a task of parsing Chinese addresses into semantically meaningful chunks using a linear-chain structure. |
| Outcome: | The proposed model is able to capture complex dependencies between labels that cannot be readily captured by a simple linear-chain structure. |
Mapping Brains with Language Models: A Survey (2023.findings-acl)
Copied to clipboard
| Challenge: | accumulated evidence for brain and language model activations remains ambiguous, but correlations with model size and quality provide grounds for cautious optimism. |
| Approach: | They examine the evidence accumulated by 30 studies spanning 10 datasets and 8 metrics to determine whether there is any overlap between brain and language model activations. |
| Outcome: | The findings suggest that representations extracted from NLP models can (partially) explain the signal found in neural data. |
Segment, Mask, and Predict: Augmenting Chinese Word Segmentation with Self-Supervision (2021.emnlp-main)
Copied to clipboard
Mieradilijiang Maimaiti, Yang Liu, Yuanhang Zheng, Gang Chen, Kaiyu Huang, Ji Zhang, Huanbo Luan, Maosong Sun
| Challenge: | Recent state-of-the-art (SOTA) effective neural network methods have been used in Chinese word segmentation (CWS) However, the robustness of the previous neural methods is limited by the large-scale annotated corpus. |
| Approach: | They propose a self-supervised Chinese word segmentation approach with a straightforward and effective architecture. |
| Outcome: | The proposed approach outperforms previous methods on 9 different CWS datasets with single criterion training and multiple criteria training and achieves better robustness. |
CWSeg: An Efficient and General Approach to Chinese Word Segmentation (2023.acl-industry)
Copied to clipboard
| Challenge: | Existing methods for Chinese word segmentation have achieved state-of-the-art performance, but they pose challenges in the deployment. |
| Approach: | They propose to augment PLM-based Chinese word segmentation schemes by developing cohort training and versatile decoding strategies. |
| Outcome: | The proposed model can be used to augment existing PLM-based models and improve their performance on Chinese LLaMA and Alpaca datasets. |
The Possible, the Plausible, and the Desirable: Event-Based Modality Detection for Language Processing (2021.acl-long)
Copied to clipboard
| Challenge: | Existing studies restrict modal expressions to a closed syntactic class . modal sense labels are vastly different across different studies, lacking an accepted standard . |
| Approach: | They propose a task where modal expressions can be words of any syntactic class and sense labels are drawn from a comprehensive taxonomy which harmonizes the modal concepts contributed by the different studies. |
| Outcome: | The proposed task is based on the Georgetown Gradable Modal Expressions corpus . it detects and classifies fine-grained modal concepts and associates them with modified events . |
Are Neural Networks Extracting Linguistic Properties or Memorizing Training Data? An Observation with a Multilingual Probe for Predicting Tense (2021.eacl-main)
Copied to clipboard
| Challenge: | a recent study has shown that neural networks can learn from linguistic representations without supervision . many studies have tried to identify which linguistic properties are encoded in the embeddings . |
| Approach: | They evaluate the ability of Bert embeddings to represent tense information . they use a multilingual linguistic probe to predict the morphology of a word . |
| Outcome: | The proposed model can predict tenses in French and Chinese, but the results drop sharply for Chinese. |