Inspired by early research on exploring naturally annotated data for Chinese Word Segmentation (CWS), and also by recent research on integration of speech and text processing, this work for the first time proposes to explicitly mine word boundaries from parallel speech-text data. We employ the Montreal Forced Aligner (MFA) toolkit to perform character-level alignment on speech-text data, giving pauses as candidate word boundaries. Based on detailed analysis of collected pauses, we propose an effective probability-based strategy for filtering unreliable word boundaries. To more effectively utilize word boundaries as extra training data, we also propose a robust complete-then-train (CTT) strategy. We conduct cross-domain CWS experiments on two target domains, i.e., ZX and AISHELL2. We have annotated about 1K sentences as the evaluation data of AISHELL2. Experiments demonstrate the effectiveness of our proposed approach.

It is practically useful to provide consistent and reliable word segmentation results from different criteria at the same time, which is formulated as the multi-grained word segmentation (MWS) task. This paper describes a probabilistic toolkit for MWS in Chinese. We propose a new MWS approach based on the standard MTL framework. We adopt semi-Markov CRF for single-grained word segmentation (SWS), which can produce marginal probabilities of words during inference. For sentences that contain conflicts among SWS results, we employ the CKY decoding algorithm to resolve conflicts.Our resulting MWS tree can provide the criteria information of words, along with the probabilities. Moreover, we follow the works in SWS, and propose a simple strategy to exploit naturally annotated data for MWS, leading to substantial improvement of MWS performance in the cross-domain scenario.

This paper describes our system for the EvaHan2024 shared task. We design and experiment with two sequence labeling approaches, i.e., one-stage and two-stage approaches. The one-stage approach directly predicts a label for each character, and the label may contain multiple punctuation marks. The two-stage approach divides punctuation marks into two classes, i.e., pause and non-pause, and separately handles them via two sequence labeling processes. The labels contain at most one punctuation marks. We use pre-trained SikuRoBERTa as a key component of the encoder and employ a conditional random field (CRF) layer on the top. According to the evaluation metrics adopted by the organizers, the two-stage approach is superior to the one-stage approach, and our system achieves the second place among all participant systems.

Event detection (ED), a key subtask of information extraction, aims to recognize instances of specific event types in text. Previous studies on the task have verified the effectiveness of integrating syntactic dependency into graph convolutional networks. However, these methods usually ignore dependency label information, which conveys rich and useful linguistic knowledge for ED. In this paper, we propose a novel architecture named Edge-Enhanced Graph Convolution Networks (EE-GCN), which simultaneously exploits syntactic structure and typed dependency label information to perform ED. Specifically, an edge-aware node update module is designed to generate expressive word representations by aggregating syntactically-connected words through specific dependency types. Furthermore, to fully explore clues hidden from dependency edges, a node-aware edge update module is introduced, which refines the relation representations with contextual information. These two modules are complementary to each other and work in a mutual promotion way. We conduct experiments on the widely used ACE2005 dataset and the results show significant improvement over competitive baseline methods.