Few-shot Incremental Event Detection

Event detection tasks can help people quickly determine the domain from complex texts. It can also provides powerful support for downstream tasks of natural language processing.Existing methods implement fixed-type learning only based on large amounts of data. When extending to new classes, it is often required to retain the original data and retrain the model.Incremental event detection tasks enables lifelong learning of new classes, but most existing methods need to retain a large number of original data or face the problem of catastrophic forgetting. Apart from that, it is difficult to obtain enough data for model training due to the lack of high-quality data in practical.To address the above problems, we define a new task in the domain of event detection, which is few-shot incremental event detection.This task require that the model should retain previous type when learning new event type in each round with limited input. We recreate and release a benchmark dataset in the few-shot incremental event detection task based on FewEvent.The dataset we published is more appropriate than other in this new task. In addition, we propose two benchmark approaches, IFSED-K and IFSED-KP, which can address the task in different ways. Experiments results have shown that our approach has a higher F1 score and is more stable than baseline.

Chinese Spelling Error Detection Using a Fusion Lattice LSTM

Spelling error detection serves as a crucial preprocessing in many natural language processing applications. Due to the characteristics of Chinese Language, Chinese spelling error detection is more challenging than error detection in English. Existing methods are mainly under a pipeline framework, which artificially divides error detection process into two steps. Thus, these methods bring error propagation and cannot always work well due to the complexity of the language environment. Besides existing methods only adopt character or word information, and ignore the positive effect of fusing character, word, pinyin1 information together. We propose an LF-LSTM-CRF model, which is an extension of the LSTMCRF with word lattices and character-pinyin-fusion inputs. Our model takes advantage of the end-to-end framework to detect errors as a whole process, and dynamically integrates character, word and pinyin information. Experiments on the SIGHAN data show that our LF-LSTM-CRF outperforms existing methods with similar external resources consistently, and confirm the feasibility of adopting the end-to-end framework and the availability of integrating of character, word and pinyin information.