SlowGuess/NAACL_2022_OCR huggingface dataset:
pseudo-labeled data is involved. The hyperparameters are fixed across different experiments: we set the initial learning rate, batch size, and max sequence length to 3e-5, 32, and 512, respectively.
5.3 Results on Existing SI Datasets
We first evaluate our extractive method E2E_SI on two existing SI datasets and CSI. We run each experiment five times using different random seeds. As shown in Table 5 (standard deviation in parentheses), E2E_SI based on RoBERTa-wwm-large can already obtain comparable or better performance than that of state-of-the-art methods, despite there is not much room for improvement for JY. Furthermore, it does not require a given small number of human-selected candidate speakers for each instance or a list of book-level character names and their alternative names as previous SI methods (Chen et al., 2021; Jia et al., 2021). We use RoBERTa-wwm-large in the remaining experiments due to its superior SI performance. Transfer learning with CSI leads to gains on both datasets.
5.4 Discussions on Continual Self-Training
Table 6 shows that continual self-training, which continuously leverages large-scale (around 793K) pseudo-labeled instances, leads to  $+2.3\%$  in exact match on the CSI dataset (4 vs. 0). In particular, we observe that the resulting student model after each iteration consistently outperforms its teacher model (e.g., 2 vs. 1 and 4 vs. 3).
Slow Growth and Potential Solutions: Just as standard self-training, the performance will inevitably grow slowly or stop increasing after iterations over either changing or fixed corpora. Inspired by previous work that combined self-training and active learning (AL) for other tasks such as entity (Tomanek and Hahn, 2009) and time expres
method	pre-trained language model	usage of rules	require list of speakers	WP	JY
dev	test	dev	test
Random	N/A	N/A	YES	-	37.6†	-	33.7§
Rule	N/A	purely rule-based	YES	-	-	-	86.6§
SVM	N/A	rule-based features	YES	-	-	-	94.5§
MLP	N/A	rule-based features	YES	-	70.5†	-	95.6§
CSN	BERT-base	post-processing	YES	-	82.5†	-	-
our end-to-end methods:
E2E_SI	BERT-wwm-ext-base	N/A	NO	65.3 (1.1)	64.7 (2.0)	96.9 (0.1)	97.0 (0.1)
E2E_SI	RoBERTa-wwm-large	N/A	NO	78.6 (1.9)	80.9 (0.9)	98.1 (0.1)	98.3 (0.1)
E2E_SI	0 in Table 6	N/A	NO	85.1 (1.0)	86.0 (0.9)	98.2 (0.1)	98.4 (0.0)
Table 5: Performance (accuracy or exact match) (%) of speaker identification methods on three speaker identification datasets for Chinese  $\dagger$ : numbers reported by Chen et al. (2021); §: numbers reported by Jia et al. (2021)).
id	method	training data	dev data	teacher	initialization	# of epochs	F1	EM
0	clean	clean	-	-	5	91.0 (0.3)	89.5 (0.3)
1	E2E_SI+CST	clean + CORPUS_1	clean	0	-	1	92.1 (0.1)	90.9 (0.1)
2	clean + CORPUS_2	clean	1	1	1	92.5 (0.1)	91.3 (0.1)
3	clean + CORPUS_3	clean	2	2	1	92.7 (0.1)	91.6 (0.2)
4	clean + CORPUS_4	clean	3	3	1	92.9 (0.1)	91.8 (0.1)
1A	E2E_SI+CST	clean + CORPUS_1	clean	0	0	1	92.0 (0.1)	90.8 (0.1)
1B	clean + CORPUS_2	clean	1A	1A	1	92.4 (0.2)	91.2 (0.2)
2A	E2E_SI+CST	clean + CORPUS_2	clean	1	-	1	92.3 (0.1)	91.0 (0.1)
3A	E2E_SI+CST	clean + CORPUS_3	clean	2	-	1	92.3 (0.0)	91.2 (0.1)
4A	E2E_SI+CST	clean + CORPUS_4	clean	3	-	1	92.5 (0.1)	91.5 (0.2)
5A	E2E_SI+ST	clean + CORPUS_1	clean	1	1	1	92.2 (0.1)	91.0 (0.1)
5B	clean + CORPUS_1	clean	5A	5A	1	92.2 (0.0)	91.0 (0.1)
6	E2E_SI+ST	clean + CORPUS_{\{1-2\}}	clean	0	-	1	92.1 (0.1)	90.9 (0.1)
7	E2E_SI+ST	clean + CORPUS_{\{1-4\}}	clean	0	-	1	92.3 (0.1)	91.1 (0.1)
sion recognition (Su et al., 2021). We use the best-performing model (i.e., 4 in Table 6) to predict the labels for unlabeled corpora. As a preliminary experiment, we regard the instances (4,123 in total) with posterior probability smaller than a fixed value (0.5) as challenging instances and manually adjust pseudo labels of these instances only to save annotation efforts. Not surprisingly, the model underperform on these difficult instances (only  $43.4\%$  in EM and  $49.2\%$  in F1). We add the newly annotated data into the training data of CSI and conduct CST from the very beginning to fully leverage the large-scale corpora. After iterations, AL leads to an  $0.5\%$  improvement to  $93.4\%$  in F1. See results after each iteration in Table 9 (Appendix A.3). Based on the positive results, active learning may be a good solution for future improvements.
Zero-Shot Abilities: Based on the same model E2R_SI, we find that continual self-training (with or without AL) improves the model's zero-shot domain adaptation performance (the best model based on the dev performance on CSI is used for experiments). As shown in Table 7, by using CST with AL for training, E2R_SI can achieve previous SOTA performance without being trained on in-
domain data of JY. These results show the potential usefulness of our paradigm for real-world applications where training and test data can be collected from different books or sources.
Table 6: Performance (%) of speaker identification on the dev set of our annotated data CSI using self-training (ST) and continual self-training (CST).
model	notes	setting	WP	JY
E2E_SI	0 in Table 6	zero-shot	62.1	88.6
E2E_SI+CST	4 in Table 6	zero-shot	70.1	92.9
E2E_SI+CST+AL	4 in Table 9	zero-shot	75.5	95.6
E2E_SI	Table 5	full-shot	86.0	98.4
previous SOTA	Table 5	full-shot	82.5	95.6
Table 7: Domain adaptation performance (EM %) on the test set of JY and WP.
Student Initialization: In our experiments, we find that student initialization plays an essential role in continual self-training for SI. It is always helpful to use a teacher model (e.g., 1, 2, and 3 in Table 6) that has seen large-scale pseudo-labeled data to initialize the weights of its student. For example, just using the pre-trained language model to initialize 2 and 3 hurts F1 by  $0.2\%$  (2 vs. 2A) and  $0.4\%$  (3 vs. 3A), respectively. This finding, at first glance, seems to be contrary to those in some previous studies (e.g., (Xie et al., 2020; Yu et al., 2021)) that also leverage pseudo-labeled or distantly-labeled data.
This is perhaps because in CST teachers (except for 0) are trained with pseudo-labeled data constructed based on DIFFERENT sets of books, instead of relying on a FIXED set of unlabeled or distantly-labeled resources. Under this varying data condition, initializing a student by its teacher that is typically used to save training time (Zoph et al., 2020), here helps transfer out-of-domain knowledge in corpora unseen to the student via model parameters.
In contrast, training the first student model 1 on the combination of human-labeled data and pseudo-labeled instances from the pre-trained language model work slightly better than initializing it with its teacher 0, which is only trained on human-labeled instances (1 vs. 1A). This is perhaps not surprising as the clean data is also used to train 1, making knowledge transfer via parameters less necessary. And the performance difference exists as the number of iterations increases if the resulting student is put back as the teacher for continual self-training (2 vs. 1B). Thus, we train 1 without using its teacher 0 for initialization.
5.5 Comparing ST and CST
We find that iterative training based on a fixed set of unlabeled instances is less effective than expected. For example, additional iteration over CORPUS_1 leads to  $+0.1\%$  in F1 (5A vs. 1 in Table 6), while using the same teacher 1 to relabel the unlabeled CORPUS_2 that has similar number of instances as that of CORPUS_1 leads to higher gains  $+0.4\%$  (2 vs. 1). Further iterations over CORPUS_1 does not improve performance (5B vs. 5A). Inspired by previous work, we also apply data augmentation (DA) based on a fixed set of books. We augment data by using contents in a six-paragraph sliding window as context, in which the center is a candidate utterance paragraph. Still, this strategy is less effective than simply moving to new corpora (Figure 3). Considering the facts that large-scale unlabeled corpora are usually available, and we can easily generate a large number of unlabeled instances based on a single book, it seems more effective to apply CST than sticking to a single corpus with or without using DA strategies.
Under conditions with a fixed unlabeled corpus, we still have similar observations that CST upon changing subsets of the corpora yields additional gains (e.g.,  $+0.4\%$  (6 vs. 2)), especially when the size of the unlabeled data is much larger than that of the human-annotated, clean data (e.g.,  $+0.5\%$
Figure 3: Comparing CST with ST over a fixed corpus.
(7 vs. 4) when all the unlabeled corpora are used). It may be helpful to set a pre-defined ratio of the unlabeled and labeled data across iterations (Xu et al., 2021) for stronger supervision.
5.6 Error Analysis
Errors appear frequently when a pronoun that refers to the speaker is dropped for simplicity (i.e. zero pronoun (Chen and Ng, 2013)). In the following example, "Lian Shu" is mistakenly regarded as the speaker of the utterance paragraph in curly brackets, and  $\Theta$  refers to an ignored pronoun pointing to "Uncle Zheng". It may be useful to convert zero pronoun data (Yang et al., 2019) into SI formats to infuse this kind of knowledge into SI models. More examples are in Appendix A.4.
Lian Shu stood up generously, stretched out her hand, and swept the hair hanging on her chest. Uncle Zheng quickly explained: "Aren't you busy with decoration at that time? I asked the construction team for the materials. I have inquired about the nearby store. Even the lady was enthusiastic and gave me a lot of ideas. And..." (Suddenly  $\Theta$  remembered something, walked to the front desk quickly, and took a picture to show Ren Luo: "Miss Lian drew it. The interior space planning is more rigorous than I thought.") Ren Luo glanced and said, "The painting is great."
We reformulate the SI task and propose a new annotation guideline that does not require document-level 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.
We would like to thank the anonymous meta-reviewers/reviewers for their insightful feedback.
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A.1 Details about Books in CSI
Copyrights of novels used in this paper belong to their respective owners. The authors use the data for non-commercial research purposes and follow the principle of fair use. The authors will not reproduce, republish, distribute, transmit, or link data used on any other website without the express permission of respective owners. The authors bear the responsibility to comply with the rules of copyright holders.
set	book	author	year	tags
train	致我们单纯的小美好	赵乾乾	2010	youth
狼来了	金大	2011	romance
清穿日常	多木木多	2013	time travel
男多女少真可怕	金大	2013	fantasy
河神：鬼水怪谈	天下霸唱	2013	thriller
犯罪心理	长洱	2015	suspenseful
残次品	priest	2017	interstellar
死亡万花筒	西子绪	2018	supernatural
dev	最后的女神	君子以泽	2010	fantasy
蕾拉的噩梦	多木木多	2011	fan-fiction
大英雄时代	priest	2013	interstellar
半妖司藤	尾鱼	2014	supernatural
脱轨	priest	2014	fantasy
我的曼达林	墨宝非宝	2016	romance
向师祖献上咸鱼	扶华	2019	time travel
深藏不露	退戈	2020	alternate history
Table 8:Involved books in CSI.
A.2 Impacts of Context Selection: Sliding Window or Narrative Window
We compare narrative window and sliding window when we select context for unlabeled instances. We first use a six-paragraph window as such a window and narrative window results in context of similar length (180 tokens) based on our analysis (Section 3.3). Based on all the external corpora (CORPUS_1-4), though using sliding window can generate  $45.1\%$  more unlabeled instances (1,151,263 vs. 793,254), the final performance after CST is slightly worse than that of using data constructed by narrative window (Figure 3), demonstrating the advantage of using narrative window in CST. Using a longer window may hurt the performance. For example, using a ten-paragraph window, the performance of CST with all unlabeled corpora stagnates around  $92.3\%$ , as long-text understanding is also quite challenging.
A.3 Combining Active Learning and Continual Self-Training
We report detailed results in Table 9.
Figure 4: The performance of CST using narrative window and six-paragraph sliding window for context.
It is also difficult for the model to identify the speakers for a target utterance among conjunctive utterances. Conversational patterns are widely used to solve this kind of implicit speakers (Muzny et al., 2017): for example, if the i-th utterance is linked to speaker A, the i+2-th utterance is supposed to be spoken by A when the paragraph between the two utterances is also an utterance. In the following example, our system fails to extract the correct speaker for "I'm sorry". It is relatively easy to know that there are two speakers in the conversation and the speaker of the last utterance is spoken by "Lin Zhaoxi", which can help us infer the correct speaker "Lu Zhihao".
The boy was lying on the stretcher and looked at her with pitiful black eyes with a little wet, and opened his mouth in pain. {The first sentence was: "I'm sorry."} "It's okay." "Thank you." "You're welcome." "Thank you very much." "You're very welcome." Lin Zhaoxi replied fluently. Lu Zhihao's lips moved, while his words were choked.
id	method	training data	dev data	teacher	initialization	# of epochs	F1	EM
0	clean + AL_4K	clean	-	-	5	91.7 (0.1)	90.3 (0.2)
1	clean + AL_4K + CORPUS_1	clean	0	-	1	92.5 (0.2)	91.3 (0.2)
2	E2E_SI+CST+AL	clean + AL_4K + CORPUS_2	clean	1	1	1	93.0 (0.1)	91.8 (0.2)
3	clean + AL_4K + CORPUS_3	clean	2	2	1	93.2 (0.2)	92.1 (0.2)
4	clean + AL_4K + CORPUS_4	clean	3	3	1	93.4 (0.1)	92.3 (0.1)
Table 9: Performance (%) of speaker identification on the dev set of our annotated data CSI using continual self-training (CST) and active learning (AL) (AL_4K denotes the 4,123 newly labeled challenging SI instances).