A Large-Scale C hinese Multimodal NER Dataset with Speech Clues (2021)

Abstract: In this paper, we aim to explore an uncharted territory, which is Chinese multimodal named entity recognition (NER) with both textual and acoustic contents. To achieve this, we construct a large-scale human-annotated Chinese multimodal NER dataset, named CNERTA. Our corpus totally contains 42,987 annotated sentences accompanying by 71 hours of speech data. Based on this dataset, we propose a family of strong and representative baseline models, which can leverage textual features or multimodal features. Upon these baselines, to capture the natural monotonic alignment between the textual modality and the acoustic modality, we further propose a simple multimodal multitask model by introducing a speech-to-text alignment auxiliary task. Through extensive experiments, we observe that: (1) Progressive performance boosts as we move from unimodal to multimodal, verifying the necessity of integrating speech clues into Chinese NER. (2) Our proposed model yields state-of-the-art (SoTA) results on CNERTA, demonstrating its effectiveness. For further research, the annotated dataset is publicly available at http://github.com/DianboWork/CNERTA .

## dataset acquisition and comparison in this work, we aim to explore chinese ner with both speech and textual clues. but we are not aware of any such existing corpus, hence we are motivated to collect one. in this section, we will discuss the data acquisition process, subsequently present statistics of the dataset and compare the annotated dataset with other widely-used ner datasets. ## baselines based on the annotated dataset, a family of strong and representative baselines is established, including (1) text-only models presented in section 5.1, (2) lexicon-enhanced models shown in section 5.2 and (3) multimodal models introduced in section 5.3. ## proposed method previous multimodal methods ignore a natural monotonic alignment between the acoustic modality and the textual modality. to capture this alignment, we propose a multimodal multitask model, called m3t. the framework of the proposed method is shown in figure 2 . in the m3t model, we adopt the cma module to fuse acoustic information into the textual representations. besides, a ctc project layer is built upon the acoustic encoder, and the loss function is a combination of masked ctc loss and crf loss. specifically, through the ctc project layer, each acoustic representation s @xmath0, the ## experiments in this section, we carry out various experiments to investigate the effectiveness of introducing the acoustic modality. in addition, we empirically compare the proposed model and these baselines under different settings. following previous studies in ner @xcite , standard precision (p), recall (r) and f1-score (f1) are used as evaluation metrics. ## conclusion and future work in this paper, we explore chinese multimodal ner with both textual and acoustic contents. to achieve this, we construct a large-scale manually annotated multimodal ner dataset，named cnerta. based on this dataset, we establish a family of baseline models. furthermore, we propose a simple multimodal multitask method by introducing a speechto-text alignment auxiliary task. through extensive experiments, we prove that chinese ner models can benefit from introducing the acoustic modality and our proposed model is effective. in the future, we are interested in mining other information contained in speech, such as rhythm, emotion, pitch, accent and stress, to boost ner. meanwhile, we will also work on designing some speech-text pretraining tasks for building a largescale pretrained model with multimodal capabilities.