| Challenge: | Chinese named entity recognition (NER) is a fundamental task in information extraction. |
| Approach: | They propose a lattice-structured LSTM model for Chinese named entity recognition (NER) model leverages word and word sequence information to encode a sequence of input characters and all potential words that match a lexicon. |
| Outcome: | The proposed model outperforms word-based and character-based models on Chinese NER datasets. |
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An Encoding Strategy Based Word-Character LSTM for Chinese NER (N19-1)
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| Challenge: | Existing word-based model can not be trained in batches due to its DAG structure. |
| Approach: | They propose a lattice model that integrates word information into the start or end characters of a word and integrates it into a fixed-sized representation for efficient batch training. |
| Outcome: | The proposed model outperforms other state-of-the-art models on benchmark datasets and shows that it can be trained in batches without a shortcut path. |
Simplify the Usage of Lexicon in Chinese NER (2020.acl-main)
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| Challenge: | Named entity recognition (NER) is concerned with the identification of named entities in unstructured text. |
| Approach: | They propose a method for incorporating word lexicon into character representations . experimental results show method can be easily incorporated with pre-trained models . |
| Outcome: | The proposed method achieves 6.15 times faster inference speed and better performance on four benchmark Chinese NER datasets. |
Subword Encoding in Lattice LSTM for Chinese Word Segmentation (N19-1)
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| Challenge: | Using subwords, Chinese word segmentation models use character combination information to disambiguate characters. |
| Approach: | They propose a subword-based neural word segmentor that integrates character embeddings into a Lattice LSTM network over a character sequence. |
| Outcome: | The proposed model can utilize abundant character combination information, which is effective to disambiguate characters. |
State-of-the-art Chinese Word Segmentation with Bi-LSTMs (D18-1)
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| 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. |
FLAT: Chinese NER Using Flat-Lattice Transformer (2020.acl-main)
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| Challenge: | Named entity recognition (NER) models are difficult to use because of the complex nature of the lattice structure and the low inference speed. |
| Approach: | They propose a character-word lattice structure that converts lattics into flat structures consisting of spans. |
| Outcome: | The proposed model outperforms other lexicon-based models on four datasets and is highly parallel. |
Porous Lattice Transformer Encoder for Chinese NER (2020.coling-main)
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| Challenge: | Existing methods to integrate word boundary information into character-level Chinese NER are inefficient and lack semantic interaction. |
| Approach: | They propose an extension of transformer encoder that is tailored for ChineseNER to incorporate lexicons into character-level Chinese NER by lattices. |
| Outcome: | The proposed extension performs 11.4 times faster than state-of-the-art methods while retaining the rich long-term dependencies. |
Lattice Transformer for Speech Translation (P19-1)
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| Challenge: | Recent advances in sequence modeling have highlighted the strengths of the transformer architecture. |
| Approach: | They propose a general lattice transformer for speech translation where the input is the output of the automatic speech recognition (ASR) they propose 'controllable' lattica attention mechanism to consume latent representations. |
| Outcome: | The proposed model outperforms baseline and lattice LSTM on the Chinese-English translation task. |
Lattice-Based Transformer Encoder for Neural Machine Translation (P19-1)
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| Challenge: | Neural machine translation (NMT) takes deterministic sequences for source representations. However, word-level or subword-level segmentation has multiple choices to split a source sequence with different word segmentors or different subword vocabulary sizes. |
| Approach: | They propose lattice-based encoders to explore effective word or subword representations in an automatic way during training. |
| Outcome: | The proposed encoders can explore effective word or subword representation in an automatic way during training. |
Chinese WPLC: A Chinese Dataset for Evaluating Pretrained Language Models on Word Prediction Given Long-Range Context (2021.emnlp-main)
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| Challenge: | Existing datasets for word prediction with long-range context have not been tested. |
| Approach: | They propose automatic and manual selection strategies tailored to Chinese to ensure that target words can only be predicted with long-term context. |
| Outcome: | The proposed model is 45 points behind human in terms of top-1 word prediction accuracy. |
Input Representations for Parsing Discourse Representation Structures: Comparing English with Chinese (2021.acl-short)
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| Challenge: | Neural semantic parsers have obtained acceptable results in parsing DRSs . previous studies have focused on parse of DRS in English, but have focused only on a few languages . |
| Approach: | They propose to use character sequences as input to map meaning representations to string format. |
| Outcome: | The proposed models learn the meaning of a series of semantic phenomena by taking sentences as input and outputting the corresponding DRSs, without the aid of any extra linguistic information. |