End-to-End C hinese Speaker Identification (2022)

Abstract: Speaker identification (SI) in texts aims to identify the speaker(s) for each utterance in texts. Previous studies divide SI into several sub-tasks (e.g., quote extraction, named entity recognition, gender identification, and coreference resolution). However, we are still far from solving these sub-tasks, making SI systems that rely on them seriously suffer from error propagation. End-to-end SI systems, on the other hand, are not limited by individual modules, but suffer from insufficient training data from the existing small-scale datasets. To make large end-to-end models possible, we design a new annotation guideline that regards SI as span extraction from the local context, and we annotate by far the largest SI dataset for Chinese named CSI based on eighteen novels. Viewing SI as a span selection task also introduces the possibility of applying existing storng extractive machine reading comprehension (MRC) baselines. Surprisingly, simply using such a baseline without human-annotated character names and carefully designed rules, we can already achieve performance comparable or better than those of previous state-of-the-art SI methods on all public SI datasets for Chinese. Furthermore, we show that our dataset can serve as additional training data for existing benchmarks, which leads to further gains (up to 6.5% in accuracy). Finally, using CSI as a clean source, we design an effective self-training paradigm to continuously leverage hundreds of unlabeled novels.

## introduction speaker identification (si) aims to identify the corresponding speakers for utterances in texts @xcite . most existing si datasets @xcite provide ground-truth character aliases and utterance spans as inputs. however, such annotations are unavailable in realistic settings, under which si is usually divided into interrelated sub-tasks @xcite (e.g., utterance identification, named entity recognition, coreference resolution, and candidate speaker generation). however, this pipeline faces several challenges. first, these modules are imperfect, and they inevitably introduce errors that propagate and seriously affect the final performance (c-i). for example, the performance of the state-of-the-art coreference resolution model is about 80.3% in f1 @xcite . second, classical si datasets and approaches assume that the speaker to be linkable to one of the named entities, which cannot handle the more realistic settings where the speakers are not humans or only exist as nominals (e.g., "a young girl" or "smartwatch") (c-ii). third, features (e.g., speech verb list and position information) and rules are usually carefully created and selected by experts for a certain language, which may make these resources difficult to be used for other languages (c-iii). finally, one of the main reasons that people heavily rely on pipeline methods is that book-level exhaustive annotations of si datasets are too expensive. as a result, existing small-scale annotations are insufficient to train large models, especially the advanced pre-trained language models @xcite . this work focuses on the abovementioned four challenges. we first design a new annotation guideline that simplifies the task to span extraction from the local context and thus viewing si as an end-toend task (c-i): given a snippet that contains several contiguous paragraphs, for each paragraph that may contain utterances, we annotate the most informative reference to a speaker (i.e., speaker mention) if one exists, otherwise the content within quotation marks. as a result, speaker mentions are not limited to entities only (c-ii). this simplification is built on two assumptions: utterances in a single paragraph usually correspond to a single speaker @xcite (e.g., all utterances in paragraph u1 in considering the similarity between simplified si and extractive machine reading comprehension (mrc), which aims to extract an answer span from a given document for a question @xcite , we can easily adapt an mrc baseline to si, which does not consider any language-specific features (c-iii). surprisingly, simply using such an extractive method @xcite ) already yields comparable or better performance than that of state-of-the-art systems on two public si datasets for chinese (wp @xcite and jy @xcite ). furthermore, our experimental results demonstrate that csi can serve as additional high-quality training data for existing si datasets, though it follows an extractive annotation guideline based on local context, which is quite different from that of traditional book-level annotation. finally, using csi as a clean source, we develop a simple yet effective self-training paradigm to continue leveraging hundreds of unlabeled novels (c-iv), which further reduces the gap between supervised and zero-shot performance on wp and jy. 1 the contributions of this paper are as follows. n1 layla didn't give brandy anything dangerous, so she put away things like a silver knife. she only asked her to use a mill, and then brandi was able to sit there and grind all the peppers in the house into powder. ## conclusions we reformulate the si task and propose a new annotation guideline that does not require documentlevel understanding. we apply an extractive mrc method to si, which outperforms previous sota methods on all existing si datasets for chinese. finally, we propose continual self-training to continue learning from new out-of-domain unlabeled corpora and combine active learning with this paradigm for further improvements.