SlowGuess/ACL_2020_OCR huggingface dataset:
Effect of Pre-training Mention Proposal Network Skipping the pre-training of the mention proposal network using golden mentions results in a 7.5 F1 degradation, which is in line with our expectation. A randomly initialized mention proposal model implies that mentions are randomly selected. Randomly selected mentions will mostly be transformed to unanswerable queries. This makes it hard for the question answering model to learn at the initial training stage, leading to inferior performance.
Effect of QA pre-training on the augmented datasets One of the most valuable strengths of
https://github.com/lessw2020/Ranger-Deep-Learning-Optimizer  
http://conll.cemantix.org/2012/software.html
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MUC	B3	CEAFφ4	Avg. F1
P	R	F1	P	R	F1	P	R	F1
e2e-coref(Lee et al., 2017)	78.4	73.4	75.8	68.6	61.8	65.0	62.7	59.0	60.8	67.2
c2f-coref + ELMo (Lee et al., 2018)	81.4	79.5	80.4	72.2	69.5	70.8	68.2	67.1	67.6	73.0
EE + BERT-large (Kantor and Globerson, 2019)	82.6	84.1	83.4	73.3	76.2	74.7	72.4	71.1	71.8	76.6
c2f-coref + BERT-large (Joshi et al., 2019b)	84.7	82.4	83.5	76.5	74.0	75.3	74.1	69.8	71.9	76.9
c2f-coref + SpanBERT-large (Joshi et al., 2019a)	85.8	84.8	85.3	78.3	77.9	78.1	76.4	74.2	75.3	79.6
CorefQA + SpanBERT-base	85.2	87.4	86.3	78.7	76.5	77.6	76.0	75.6	75.8	79.9 (+0.3)
CorefQA + SpanBERT-large	88.6	87.4	88.0	82.4	82.0	82.2	79.9	78.3	79.1	83.1 (+3.5)
Table 1: Evaluation results on the English CoNLL-2012 shared task. The average F1 of MUC,  $\mathbf{B}^3$ , and  $\mathrm{CEAF}_{\phi_4}$  is the main evaluation metric. Ensemble models are not included in the table for a fair comparison.  $P$ ,  $R$  and  $F1$  in the first row represent precision, recall and F1 score respectively.
Model	M	F	B	O
e2e-coref	67.2	62.2	0.92	64.7
c2f-coref + ELMo	75.8	71.1	0.94	73.5
c2f-coref + BERT-large	86.9	83.0	0.95	85.0
c2f-coref + SpanBERT-large	88.8	84.9	0.96	86.8
CorefQA + SpanBERT-large	88.9	86.1	0.97	87.5
Table 2: CorefQA achieves the state-of-the-art performance on all metrics including F1 scores on Masculine and Feminine examples, a Bias factor  $(\mathrm{F} / \mathrm{M})$  and the Overall F1 score.
Avg. F1	Δ
CorefQA	83.4
-- SpanBERT	79.6	-3.8
-- Mention Proposal Pre-train	75.9	-7.5
-- Question Answering	75.0	-8.4
-- Quoref Pre-train	82.7	-0.7
-- SQuAD Pre-train	83.1	-0.3
Table 3: Ablation studies on the CoNLL-2012 development set. SpanBERT token representations, the mention-proposal pre-training, and the question answering pre-training all contribute significantly to the good performance of the full model.
converting anaphora resolution to question answering is that existing QA datasets can be readily used for data augmentation purposes. We see a contribution of 0.7 F1 from pre-training on the Quoref dataset (Dasigi et al., 2019a) and a contribution of 0.3 F1 from pre-training on the SQuAD dataset (Rajpurkar et al., 2016a).
Effect of Question Answering We aim to study the pure performance gain of the paradigm shift from mention-pair scoring to query-based span prediction. For this purpose, we replace the mention linking module with the mention-pair scoring module described in Lee et al. (2018), while others
Figure 3: Performance on the development set of the CoNLL-2012 dataset with various number of speakers. F1(Speaker as feature): F1 score for the strategy that treats speaker information as a mention-pair feature. F1(Speaker as input): F1 score for our strategy that treats speaker names as token input. Frequency: percentage of documents with specific number of speakers.
remain unchanged. We observe an 8.4 F1 degradation in performance, demonstrating the significant superiority of the proposed question answering framework over the mention-pair scoring framework.
5.2 Analyses on speaker modeling strategies
We compare our speaker modeling strategy (denoted by Speaker as input), which directly concatenates the speaker's name with the corresponding utterance, with the strategy in Wiseman et al. (2016); Lee et al. (2017); Joshi et al. (2019a) (denoted by Speaker as feature), which converts speaker information into binary features indicating whether two mentions are from the same speaker. We show the average F1 scores breakdown by
Figure 4: Change of mention recalls as we increase the number of spans  $\lambda$  kept per word.
documents according to the number of their constituent speakers in Figure 3.
Results show that the proposed strategy performs significantly better on documents with a larger number of speakers. Compared with the coarse modeling of whether two utterances are from the same speaker, a speaker's name can be thought of as speaker ID in persona dialogue learning (Li et al., 2016; Zhang et al., 2018b; Mazaré et al., 2018). Representations learned for names have the potential to better generalize the global information of the speakers in the multi-party dialogue situation, leading to better context modeling and thus better results.
5.3 Analysis on the Overall Mention Recall
Since the proposed framework has the potential to retrieve mentions missed at the mention proposal stage, we expect it to have higher overall mention recall rate than previous models (Lee et al., 2017, 2018; Zhang et al., 2018a; Kantor and Globerson, 2019).
We examine the proportion of gold mentions covered in the development set as we increase the hyperparameter  $\lambda$  (the number of spans kept per word) in Figure 4. Our model consistently outperforms the baseline model with various values of  $\lambda$ . Notably, our model is less sensitive to smaller values of  $\lambda$ . This is because missed mentions can still be retrieved at the mention linking stage.
We provide qualitative analyses to highlight the strengths of our model in Table 4.
Shown in Example 1, by explicitly formulating the anaphora identification of the company as a
[Freddie Mac] is giving golden parachutes to two of its ousted executives. ... Yesterday Federal Prosecutions announced a criminal probe into [the company].
[A traveling reporter] now on leave and joins us to tell [her] story. Thank [you] for coming in to share this with us.
Paula Zahn: [Thelma Gutierrez] went inside the forensic laboratory where scientists are trying to solve this mystery.
3 Thelma Gutierrez: In this laboratory alone [I] 'm surrounded by the remains of at least twenty different service members who are in the process of being identified so that they too can go home.
Table 4: Example mention clusters that were correctly predicted by our model, but wrongly predicted by c2f-coref + SpanBERT-large. Bold spans in brackets represent coreferent mentions. Italic spans represent the speaker's name of the utterance.
query, our model uses more information from a local context, and successfully identifies Freddie Mac as the answer from a longer distance.
The model can also efficiently harness the speaker information in a conversational setting. In Example 3, it would be difficult to identify that [Thelma Gutierrez] is the correct antecedent of mention [I] without knowing that Thelma Gutierrez is the speaker of the second utterance. However, our model successfully identifies it by directly feeding the speaker's name at the input level.
In this paper, we present CorefQA, a coreference resolution model that casts anaphora identification as the task of query-based span prediction in question answering. We showed that the proposed formalization can successfully retrieve mentions left out at the mention proposal stage. It also makes data augmentation using a plethora of existing question answering datasets possible. Furthermore, a new speaker modeling strategy can also boost the performance in dialogue settings. Empirical results on two widely-used coreference datasets demonstrate the effectiveness of our model. In future work, we will explore novel approaches to generate the questions
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based on each mention, and evaluate the influence of different question generation methods on the coreference resolution task.
We thank all anonymous reviewers for their comments and suggestions. The work is supported by the National Natural Science Foundation of China (NSFC No. 61625107 and 61751209).
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