| Challenge: | Currently, word embeddings are playing a pivotal role in many natural language processing tasks. |
| Approach: | They propose a model to learn Chinese word embeddings via three-level composition . they use convolutional neural network to extract intra-character compositionality from character shape . |
| Outcome: | The proposed model performs better on word similarity, sentiment analysis, named entity recognition and part-of-speech tagging tasks. |
Similar Papers
Enhancing Pre-trained Chinese Character Representation with Word-aligned Attention (2020.acl-main)
Copied to clipboard
| Challenge: | Pre-trained Chinese language models take character as the basic unit and learn representation according to character’s external contexts, ignoring the semantics expressed in the word, which is the smallest meaningful utterance in Chinese. |
| Approach: | They propose to pool character-level attention to the word level and propose to alleviate the potential issue of segmentation error propagation by multi-source information fusion. |
| Outcome: | The proposed approach improves on five Chinese NLP benchmark tasks against BERT, ERNIE and BERT-wwm. |
Multiple Character Embeddings for Chinese Word Segmentation (P19-2)
Copied to clipboard
| Challenge: | Chinese word segmentation is regarded as character-based sequence labeling task in most current work but it neglects important fact: Chinese characters contain both semantic and phonetic meanings. |
| Approach: | They propose a shared bi-LSTM-CRF model which fuses linguistic features efficiently by sharing the LSTM network during the training procedure. |
| Outcome: | The proposed model achieves state-of-the-art in AS and CityU corpora without external lexical resources. |
Segmentation-free compositional n-gram embedding (N19-1)
Copied to clipboard
| Challenge: | Existing word embedding models depend on word segmentation, but this method is difficult when corpora written in noisy or unsegmented languages. |
| Approach: | They propose a new method that models words, phrases and sentences seamlessly without word segmentation. |
| Outcome: | The proposed method is very effective for noisy corpora written in unsegmented languages such as Chinese and Japanese. |
Learning Better Internal Structure of Words for Sequence Labeling (D18-1)
Copied to clipboard
| Challenge: | a gap exists between methods for learning representations of sentences and words . authors propose a convolutional neural architecture with no down-sampling for learning words based on character embeddings . |
| Approach: | They propose a funnel-shaped wide convolutional neural architecture with no down-sampling for learning words' internal structure. |
| Outcome: | The proposed model outperforms other character embedding models on six sequence labeling datasets. |
A Probabilistic Model for Joint Learning of Word Embeddings from Texts and Images (D18-1)
Copied to clipboard
| Challenge: | Existing approaches combine language and perception to infer word embeddings . however, the embeddables produced by such models do not reflect the actual word representations. |
| Approach: | They propose a probabilistic model that integrates linguistic and perceptual inputs to explain observed word-context pairs in a text corpus. |
| Outcome: | The proposed model achieves competitive or stronger results on tasks of assessing pairwise word similarity and image/caption retrieval compared to other state-of-the-art models. |
Breaking the Representation Bottleneck of Chinese Characters: Neural Machine Translation with Stroke Sequence Modeling (2022.emnlp-main)
Copied to clipboard
| Challenge: | Existing research treats Chinese character as a minimum unit for representation . however, such representation suffers from two bottlenecks: 1) learning bottleneck; 2) parameter bottleneck, each individual character has to be represented by a unique vector. |
| Approach: | They propose a representation method for Chinese characters to break the representation bottlenecks . they map each stroke to a specific Latin character, thus allowing similar Chinese characters . |
| Outcome: | The proposed representation method breaks two representation bottlenecks in Chinese character representation . it maps each stroke to a specific Latin character, thus allowing similar Chinese characters to have similar representations . |
Models In a Spelling Bee: Language Models Implicitly Learn the Character Composition of Tokens (2022.naacl-main)
Copied to clipboard
| Challenge: | Standard pre-trained language models do not see the characters that compose each token's string representation. |
| Approach: | They probe the embedding layer of pretrained language models and show that models learn the internal character composition of whole word and subword tokens without seeing the characters coupled with the tokens. |
| Outcome: | The embedding layers of RoBERTa and GPT2 hold enough information to accurately spell up to a third of the vocabulary and reach high character ngram overlap across all token types. |
Joint Chinese Word Segmentation and Part-of-speech Tagging via Multi-channel Attention of Character N-grams (2020.coling-main)
Copied to clipboard
| Challenge: | Chinese word segmentation and part-of-speech tagging can be performed in a sequential order . existing studies have shown that jointly performing them can be effective . |
| Approach: | They propose a character-based neural model enhanced by multi-channel attention of n-grams. |
| Outcome: | The proposed model outperforms baseline models on five benchmark datasets. |
A Systematic Study of Leveraging Subword Information for Learning Word Representations (N19-1)
Copied to clipboard
| Challenge: | Existing word representation models for morphologically rich languages use subword-level information, but their systematic comparative analysis across typologically diverse languages and tasks is still missing. |
| Approach: | They propose a framework for learning subword-informed word representations that allows for easy experimentation with different segmentation and composition components. |
| Outcome: | The proposed framework allows for easy experimentation with different segmentation and composition components, as well as advanced techniques based on position embeddings and self-attention. |
Word Embeddings for Code-Mixed Language Processing (D18-1)
Copied to clipboard
| Challenge: | Existing bilingual word embedding techniques are not ideal for code-mixed text processing and there is a need for learning multilingual word embeds from code-mixed texts. |
| Approach: | They propose to use bilingual word embedding techniques to train skip-grams on synthetic code-mixed text generated through linguistic models of code- mixing to perform two tasks. |
| Outcome: | The proposed embedding technique performs better on semantic and syntactic tasks than the existing embeddable techniques on sentiment analysis and POS tagging tasks. |