SlowGuess/NAACL_2022_OCR huggingface dataset:
Next, we employ the Google text-to-speech system to transform the questions and documents in CoQA into the spoken form, and adopt CMU Sphinx to transcribe the processed spoken contents into ASR transcriptions. In doing so, we collect more than 40G audio data, and the data duration is around 300 hours. The ASR transcription has a kappa score of 0.738 and Word Error Rates (WER) of  $15.9\%$ , which can be considered sufficiently good since it is below the accuracy threshold of  $30\%$  WER (Gaur et al., 2016). For the test set, we invite 5 human native English speakers to read the sentences of the documents and questions. The sentences of one single document are assigned to a single speaker to keep consistency, while the questions in one example may have different speakers. All speech files are sampled at  $16\mathrm{kHz}$ , following the common approach in the speech community. We provide an example of our Spoken-CoQA dataset in Table 1 and Fig. 5.
Data Filtering In our SCQA task, the model predicts the start and end positions of answers in the ASR transcriptions. As a result, during data construction, it is necessary for us to perform data filtering by eliminating question-answer pairs if the answer spans to questions do not exist in the noisy ASR transcriptions. We follow the conventional settings in (Lee et al., 2018) $^3$ . In our approach, an ASR question will be removed if the groundtruth answers do not exist in ASR passages. However, when coreference resolution and inference occurs, the contextual questions related to the previous ones are required to be discarded too. For the case of coreference resolution, we change the corresponding coreference. For the case of coreference inference, if the question has strong dependence on the previous one that has already been discarded, it will also be removed. After data filtering, we get a total number of our Spoken-CoQA dataset, we collect 4k conversations in the training set, and 380 conversations in the test set in our Spoken-CoQA dataset, respectively. Our dataset includes 5 domains, and we show the domain distributions in Table 3.
Baselines. For SCQA tasks, our DDNET is able to utilize a variety of backbone networks for SCQA
2Considering that the test set of CoQA (Reddy et al., 2019) idoes not publicly availablesh the test set, we follow the widely used setting in the spoken question answering task (Li et al., 2018), where we divide Spoken-CoQA dataset into train and test set.
<sup>3</sup>We compare different Speech APIs, e.g., Google and CMU. Considering the quality of generated speech transcripts, we choose Google TTS for TTS and CMU Sphinx for ASR.
CoQA	S-CoQA
Methods	CoQA dev	S-CoQA test	CoQA dev	S-CoQA test
EM	F1	EM	F1	EM	F1	EM	F1
FlowQA (Huang et al., 2018a)	66.8	75.1	44.1	56.8	40.9	51.6	22.1	34.7
SDNet (Zhu et al., 2018)	68.1	76.9	39.5	51.2	40.1	52.5	41.5	53.1
BERT-base (Devlin et al., 2018)	67.7	77.7	41.8	54.7	42.3	55.8	40.6	54.1
ALBERT-base (Lan et al., 2020)	71.4	80.6	42.6	54.8	42.7	56.0	41.4	55.2
Average	68.5	77.6	42	54.4	41.5	54.0	36.4	49.3
Table 4: Comparison of four baselines (FlowQA, SDNet, BERT, ALBERT). Note that we denote Spoken-CoQA test set as S-CoQA test for brevity.
tasks. We choose several state-of-the-art language models (FlowQA (Huang et al., 2018a), SDNet (Zhu et al., 2018), BERT-base (Devlin et al., 2018), ALBERT (Lan et al., 2020)) as our backbone network baselines. We also compare our proposed DDNET with several state-of-the-art SQA methods (Lee et al., 2018; Serdyuk et al., 2018; Lee et al., 2019; Kuo et al., 2020). To use the teacher-student architecture in our models, we first train baselines on the CoQA training set as teacher and then evaluate the performances of testing baselines on CoQA dev set and Spoken-CoQA dev set. Finally, we train the baselines on the Spoken-CoQA training set as student and evaluate the baselines on the CoQA dev set and Spoken-CoQA test set. We provide quantitative results in Table 4.
Experiment Settings. We use the official BERT (Devlin et al., 2018) and ALBERT (Lan et al., 2020) as our textual embedding modules. We use BERT-base (Devlin et al., 2018) and ALBERT-base (Lan et al., 2020), which both include 12 transformer encoders, and the hidden size of each word vector is 768. BERT and ALBERT both utilize BPE as the tokenizer, but FlowQA and SDNet use SpaCy (Honnibal and Montani, 2017) for tokenization. Under the circumstance when tokens in spaCy (Honnibal and Montani, 2017) correspond to more than one BPE sub-tokens, we average the BERT embeddings of these BPE sub-tokens as the final embeddings for each token. For fair comparisons, we use standard implementations and hyper-parameters of four baselines for training. The balancing factor  $\alpha$  is set to 0.9, and the temperature  $\tau$  is set to 2. We train all models on 4 24GB RTX GPUs, with a batch size of 8 on each GPU. For evaluation, we use three metrics: Exact Match (EM),  $F_{1}$  score and Audio Overlapping Score (AOS) (Li et al., 2018) to compare the model performance comprehensively. Please note that the metric numbers of baseline may be different from that in the CoQA leader board as we use our own implementations, Note that, we only utilize the student network for inference.
Results. We compare several teacher-student pairs on CoQA and Spoken-CoQA dataset and the quantitative results are shown in Table 4. We can observe that the average F1 scores is  $77.6\%$  when training on CoQA (text document) and testing on CoQA dev set. However, when training the models on Spoken-CoQA (ASR transcriptions) and testing on Spoken-CoQA test set, the average F1 scores drops significantly to  $49.3\%$ . For FlowQA, the performance even drops by 40.4 pts in terms of F1 score. This corroborates the importance of mitigating ASR errors.
Table 5 compares our approach DDNET to all the previous results. As shown in the table, our distillation models achieve strong performance, and incorporating DA mechanism further improves the results considerably. Our DDNET using BERT-base models as backbone achieves similar or better results compared to all the state-of-the-art methods, and we observe that using a larger encoder ALBERT-base will give further bring large gains on performance.
As seen from Table 6, we find that our best model ALBERT-base only trained with KD achieve an absolute EM/F1 improvement of  $+1.7\mathrm{pts} / + 1.7\mathrm{pts}$ ,  $+2.5\mathrm{pts} / + 2.5\mathrm{pts}$ , on CoQA and S-CoQA, respectively. This shows that cross-modal information is useful for the model, hence demonstrating that such information is able to build more robust contextualized cross-modal representations for the network performance improvements. As shown in Table 6, we also observe that our approach ALBERT-base only trained with DA outperforms the original method by an absolute EM/F1 of  $+1.4\mathrm{pts} / + 1.2\mathrm{pts}$ ,  $+1.8\mathrm{pts} / + 2.0\mathrm{pts}$ , on CoQA and S-CoQA, respectively. This indicates that the fine-grained alignment between audio and text learned through DA during training benefits the downstream speech-and-language tasks. Overall, our results suggest that such a network notably improves prediction performance for spoken conversational question answering tasks. Such significant improvements demonstrate the effectiveness of DDNET.
Methods	CoQA dev	S-CoQA test
EM	F1	AOS	EM	F1	AOS
FlowQA (Huang et al., 2018a)	40.9	51.6	30.6	22.1	34.7	16.7
FlowQA + sub-word unit (Li et al., 2018)	41.9	53.2	31.4	23.3	36.4	17.4
FlowQA+ SLU (Serdyuk et al., 2018)	41.2	52.0	30.6	22.4	35.0	17.1
FlowQA + back-translation (Lee et al., 2018)	40.5	52.1	30.8	22.9	35.8	17.3
FlowQA + domain adaptation (Lee et al., 2019)	41.7	53.0	31.8	23.4	36.1	17.7
FlowQA + Dual Attention	42.3	53.0	32.7	23.5	38.8	18.9
FlowQA + Knowledge Distillation	42.5	53.7	32.1	23.9	39.2	18.4
FlowQA + Dual Attention+Knowledge Distillation	44.3	55.9	34.4	26.3	42.4	21.1
SDNet (Zhu et al., 2018)	40.1	52.5	41.1	41.5	53.1	42.6
SDNet + sub-word unit (Li et al., 2018)	41.2	53.7	41.9	41.9	54.7	43.4
SDNet+ SLU (Serdyuk et al., 2018)	40.2	52.9	41.2	41.7	53.2	42.6
SDNet + back-translation (Lee et al., 2018)	40.5	53.1	41.5	42.4	54.0	42.9
SDNet + domain adaptation (Lee et al., 2019)	41.0	53.9	42.0	41.7	54.6	43.6
SDNet + Dual Attention	41.7	55.2	43.4	43.2	56.1	44.2
SDNet + Knowledge Distillation	41.7	55.6	43.6	43.6	56.7	44.3
SDNet + Dual Attention+Knowledge Distillation	44.3	57.9	44.0	45.9	59.1	46.8
BERT-base (Devlin et al., 2018)	42.3	55.8	50.1	40.6	54.1	48.0
BERT-base + sub-word unit (Li et al., 2018)	43.2	56.8	51.1	41.6	55.4	48.9
BERT-base+ SLU (Serdyuk et al., 2018)	42.5	56.1	50.3	41.0	54.6	48.1
BERT-base + back-translation (Lee et al., 2018)	42.9	56.5	50.5	41.5	55.2	48.6
BERT-base + domain adaptation (Lee et al., 2019)	43.1	57.0	51.0	41.7	55.7	49.0
aeBERT (Kuo et al., 2020)	43.0	56.9	51.5	41.8	55.6	50.3
BERT-base + Dual Attention	44.3	58.3	52.6	42.7	57.0	51.1
BERT-base + Knowledge Distillation	44.1	58.8	52.9	42.8	57.7	51.3
BERT-base + Dual Attention+Knowledge Distillation	46.5	61.1	55.1	45.6	60.1	53.6
ALBERT-base (Lan et al., 2020)	42.7	56.0	50.4	41.4	55.2	49.5
ALBERT-base + sub-word unit (Li et al., 2018)	43.7	57.2	51.2	42.6	56.8	50.3
ALBERT-base+ SLU (Serdyuk et al., 2018)	42.8	56.3	50.5	41.7	55.7	49.7
ALBERT-base + back-translation (Lee et al., 2018)	43.5	57.1	50.9	42.4	56.4	50.0
ALBERT-base + domain adaptation (Lee et al., 2019)	43.5	57.0	51.5	42.7	56.7	50.7
ALBERT-base + Dual Attention	44.7	59.4	52.0	43.8	58.4	51.3
ALBERT-base + Knowledge Distillation	44.8	59.6	52.7	43.9	58.7	51.6
ALBERT-base + Dual Attention+ Knowledge Distillation	47.3	61.9	55.5	46.1	61.3	53.6
Table 5: Comparison of key components in DDNET. We denote the model trained on speech document and text corpus as the teacher model, and the one trained on the ASR transcripts as the student model.
Methods	SQuAD dev	S-SQuAD test
EM	F1	EM	F1
FLowQA (Huang et al., 2018a)	51.9	65.7	49.1	63.9
FlowQA + DA	53.6	67.3	50.4	65.3
FLowQA+ KD	53.5	67.3	50.9	65.8
FLowQA+DA+ KD	55.6	68.8	52.8	68.0
SDNet (Zhu et al., 2018)	56.1	70.5	57.8	71.8
SDNet + DA	58.3	71.4	59.3	73.8
SDNet + KD	58.7	71.9	59.2	73.6
SDNet + DA+ KD	60.1	73.7	60.9	75.7
BERT-base (Devlin et al., 2018)	58.3	70.2	58.6	71.1
BERT-base + DA	59.9	72.8	61.0	74.1
BERT-base + KD	60.1	72.2	60.8	73.8
BERT-base + DA+ KD	62.1	74.6	63.3	76.0
ALBERT-base (Lan et al., 2020)	59.1	71.9	59.4	72.2
ALBERT-base + DA	60.5	73.1	61.2	74.2
ALBERT-base + KD	60.8	73.6	61.9	74.7
ALBERT-base + DA+ KD	62.6	75.7	64.1	77.1
Table 6: Comparison of our method. We set the model on text corpus as the teacher model, and the one on the ASR transcripts as the student model. DA and KD represent Dual Attention and knowledge distillation.
We conduct ablation studies to show the effectiveness of several components in DDNet in this section and appendix.
Multi-Modality Fusion Mechanism. To study the effect of different modality fusion mechanisms,
we introduce a novel fusion mechanism Con Fusion: first, we directly concatenate two output embeddings from speech-BERT and text-BERT models, and then pass it to the encoding layer in the following SCQA module. In Table 8, we observe that Dual Attention mechanism outperform four baselines with Con Fusion in terms of EM and F1 scores. We further investigate the effect of unimodel input. Table 8 shows that text-only performs better than speech-only. One possible reason for this performance is that only using speech features can bring additional noise. Note that speech-only (text-only) means that we only feed the speech (text) embedding for speech-BERT (text-BERT) to the encoding layer in the SCQA module.
In this paper, we have presented SCQA, a new spoken conversational question answering task, for enabling human-machine communication. We make our effort to collect a challenging dataset - SpokenCoQA, including multi-turn conversations and pas
sages in both text and speech form. We show that the performance of existing state-of-the-art models significantly degrade on our collected dataset, hence demonstrating the necessity of exploiting cross-modal information in achieving strong results. We provide some initial solutions via knowledge distillation and the proposed dual attention mechanism, and have achieved some good results on Spoken-CoQA. Experimental results show that DDNET achieves substantial performance improvements in accuracy. In future, we will further investigate the different mechanisms of integrating speech and text content, and our method also opens up the possibility for downstream spoken language tasks.
Alexei Baevski, Steffen Schneider, and Michael Auli. 2019. vq-wav2vec: Self-supervised learning of discrete speech representations. In International Conference on Learning Representations (ICLR).
Nuo Chen, Fenglin Liu, Chenyu You, Peilin Zhou, and Yuexian Zou. 2020. Adaptive bi-directional attention: Exploring multi-granularity representations for machine reading comprehension. In ICASSP.
Nuo Chen, Linjun Shou, Min Gong, Jian Pei, and Daxin Jiang. 2021a. From good to best: Two-stage training for cross-lingual machine reading comprehension. arXiv preprint arXiv:2112.04735.
Nuo Chen, Chenyu You, and Yuexian Zou. 2021b. Self-supervised dialogue learning for spoken conversational question answering. In INTERSPEECH.
Yuan-Ping Chen, Ryan Price, and Srinivas Bangalore. 2018. Spoken language understanding without speech recognition. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
Long Cheng and Chenyu You. 2016. Hybrid non-linear dimensionality reduction method framework based on random projections. In International Conference on Cloud Computing and Big Data Analysis (ICCBDA). IEEE.
Long Cheng, Chenyu You, and Liguo Chen. 2016a. Identification of power line outages based on pmu measurements and sparse overcomplete representation. In International Conference on Information Reuse and Integration (IRI). IEEE.
Long Cheng, Chenyu You, and Yani Guan. 2016b. Random projections for non-linear dimensionality reduction. International Journal of Machine Learning and Computing.
Eunsol Choi, He He, Mohit Iyyer, Mark Yatskar, Wentau Yih, Yejin Choi, Percy Liang, and Luke Zettle-moyer. 2018. Quac: Question answering in context.
In Empirical Methods in Natural Language Processing (EMNLP).
Yung-Sung Chuang, Chi-Liang Liu, and Hung-Yi Lee. 2019. SpeechBERT: Cross-modal pre-trained language model for end-to-end spoken question answering. arXiv preprint arXiv:1910.11559.
Jacob Devlin, Ming-Wei Chang, Kenton Lee, and Kristina Toutanova. 2018. Bert: Pre-training of deep bidirectional transformers for language understanding. arXiv preprint arXiv:1810.04805.
Ahmed Elgohary, Chen Zhao, and Jordan Boyd-Graber. 2018. Dataset and baselines for sequential open-domain question answering. In Empirical Methods in Natural Language Processing (EMNLP).
Yashesh Gaur, Walter S Lasecki, Florian Metze, and Jeffrey P Bigham. 2016. The effects of automatic speech recognition quality on human transcription latency. In Proceedings of the 13th Web for All Conference.
Hongyu Gong, Yelong Shen, Dian Yu, Jianshu Chen, and Dong Yu. 2020. Recurrent chunking mechanisms for long-text machine reading comprehensio. arXiv preprint arXiv:2005.08056.
Indranil Guha, Syed Ahmed Nadeem, Chenyu You, Xiaoliu Zhang, Steven M Levy, Ge Wang, James C Torner, and Punam K Saha. 2020. Deep learning based high-resolution reconstruction of trabecular bone microstructures from low-resolution ct scans using gan-circle. In Medical Imaging 2020: Biomedical Applications in Molecular, Structural, and Functional Imaging.
Parisa Haghani, Arun Narayanan, Michiel Bacchiani, Galen Chuang, Neeraj Gaur, Pedro Moreno, Rohit Prabhavalkar, Zhongdi Qu, and Austin Waters. 2018. From audio to semantics: Approaches to end-to-end spoken language understanding. In Spoken Language Technology Workshop (SLT). IEEE.
Sangchul Hahn and Heeyoul Choi. 2019. Self-knowledge distillation in natural language processing. In Proceedings of the International Conference on Recent Advances in Natural Language Processing (RANLP), pages 423-430.
Geoffrey Hinton, Oriol Vinyals, and Jeff Dean. 2015. Distilling the knowledge in a neural network. arXiv preprint arXiv:1503.02531.
Matthew Honnibal and Ines Montani. 2017. spaCy 2: Natural language understanding with Bloom embeddings, convolutional neural networks and incremental parsing.
Minghao Hu, Yuxing Peng, Furu Wei, Zhen Huang, Dongsheng Li, Nan Yang, and Ming Zhou. 2018. Attention-guided answer distillation for machine reading comprehension. arXiv preprint arXiv:1808.07644.
Hsin-Yuan Huang, Eunsol Choi, and Wen-tau Yih. 2018a. FlowQA: Grasping flow in history for conversational machine comprehension. arXiv preprint arXiv:1810.06683.
Hsin-Yuan Huang, Chenguang Zhu, Yelong Shen, and Weizhu Chen. 2017. Fusionnet: Fusing via fully-aware attention with application to machine comprehension. arXiv preprint arXiv:1711.07341.
Mingkun Huang, Yongbin You, Zhehuai Chen, Yanmin Qian, and Kai Yu. 2018b. Knowledge distillation for sequence model. Interspeech, pages 3703-3707.
Yoon Kim and Alexander M Rush. 2016. Sequence-level knowledge distillation. In Empirical Methods in Natural Language Processing (EMNLP), pages 1317-1327.
Chia-Chih Kuo, Shang-Bao Luo, and Kuan-Yu Chen. 2020. An audio-enriched bert-based framework for spoken multiple-choice question answering. arXiv preprint arXiv:2005.12142.
Zhenzhong Lan, Mingda Chen, Sebastian Goodman, Kevin Gimpel, Piyush Sharma, and Radu Soricut. 2020. ALBERT: A Lite BERT for Self-supervised Learning of Language Representations. In International Conference on Learning Representations (ICLR).
Chia-Hsuan Lee, Yun-Nung Chen, and Hung-Yi Lee. 2019. Mitigating the impact of speech recognition errors on spoken question answering by adversarial domain adaptation. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
Chia-Hsuan Lee, Shang-Ming Wang, Huan-Cheng Chang, and Hung-Yi Lee. 2018. ODSQA: Open-domain spoken question answering dataset. In Spoken Language Technology Workshop (SLT). IEEE.
Chia-Hsuan Li, Szu-Lin Wu, Chi-Liang Liu, and Hung-yi Lee. 2018. Spoken SQuAD: A study of mitigating the impact of speech recognition errors on listening comprehension. arXiv preprint arXiv:1804.00320.
Jiwei Li, Michel Galley, Chris Brockett, Jianfeng Gao, and William B Dolan. 2016. A diversity-promoting objective function for neural conversation models. In Proceedings of the Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies (NAACL-HLT).
Fenglin Liu, Xian Wu, Chenyu You, Shen Ge, Yuexian Zou, and Xu Sun. 2021a. Aligning source visual and target language domains for unpaired video captioning. IEEE Transactions on Pattern Analysis and Machine Intelligence.
Fenglin Liu, Chenyu You, Xian Wu, Shen Ge, Xu Sun, et al. 2021b. Auto-encoding knowledge graph for unsupervised medical report generation. Advances in Neural Information Processing Systems (NeurIPS).
Yinhan Liu, Myle Ott, Naman Goyal, Jingfei Du, Mandar Joshi, Danqi Chen, Omer Levy, Mike Lewis, Luke Zettlemoyer, and Veselin Stoyanov. 2019. RoBERTa: A Robustly Optimized BERT Pretraining Approach. arXiv preprint arXiv:1907.11692.
Jiasen Lu, Dhruv Batra, Devi Parikh, and Stefan Lee. 2019. Vilbert: Pretraining task-agnostic visiolinguistic representations for vision-and-language tasks. In Advances in Neural Information Processing Systems (NeurIPS), pages 13-23.
Loren Lugosch, Mirco Ravanelli, Patrick Ignoto, Vikrant Singh Tomar, and Yoshua Bengio. 2019. Speech model pre-training for end-to-end spoken language understanding. arXiv preprint arXiv:1904.03670.
Qing Lyu, Chenyu You, Hongming Shan, and Ge Wang. 2018. Super-resolution mri through deep learning. arXiv preprint arXiv:1810.06776.
Qing Lyu, Chenyu You, Hongming Shan, Yi Zhang, and Ge Wang. 2019. Super-resolution mri and ct through gan-circle. In *Developments in X-ray tomography XII*. International Society for Optics and Photonics.
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, and Zhangyang Wang. 2021a. Good students play big lottery better. arXiv preprint arXiv:2101.03255, 3.
Haoyu Ma, Tianlong Chen, Ting-Kuei Hu, Chenyu You, Xiaohui Xie, and Zhangyang Wang. 2021b. Undistinguishable: Making a nasty teacher that cannot teach students. arXiv preprint arXiv:2105.07381.
Elisavet Palogiannidi, Ioannis Gkinis, George Mastrapas, Petr Mizera, and Themos Stafylakis. 2020. End-to-end architectures for asr-free spoken language understanding. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
Siva Reddy, Danqi Chen, and Christopher D Manning. 2019. CoQA: A Conversational Question Answering Challenge. Transactions of the Association for Computational Linguistics.
Dmitriy Serdyuk, Yongqiang Wang, Christian Fuegen, Anuj Kumar, Baiyang Liu, and Yoshua Bengio. 2018. Towards end-to-end spoken language understanding. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
Dan Su and Pascale Fung. 2020. Improving spoken question answering using contextualized word representation. In International Conference on Acoustics, Speech and Signal Processing (ICASSP), pages 8004-8008. IEEE.
Kun Su, Xiulong Liu, and Eli Shlizerman. 2020. Audio generation for a silent performance video. Advances in Neural Information Processing Systems (NeurIPS).
Kun Su, Xiulong Liu, and Eli Shlizerman. 2021. How does it sound? Advances in Neural Information Processing Systems (NeurIPS).
Bo-Hsiang Tseng, Sheng-Syun Shen, Hung-Yi Lee, and Lin-Shan Lee. 2016. Towards machine comprehension of spoken content: Initial TOEFL listening comprehension test by machine. arXiv preprint arXiv:1608.06378.
Ashish Vaswani, Noam Shazeer, Niki Parmar, Jakob Uszkoreit, Llion Jones, Aidan N Gomez, Lukasz Kaiser, and Illia Polosukhin. 2017. Attention is all you need. In Advances in neural information processing systems (NeurIPS).
Hu Xu, Bing Liu, Lei Shu, and Philip S Yu. 2019. Review conversational reading comprehension. arXiv preprint arXiv:1902.00821.
Weiyuan Xu, Peilin Zhou, Chenyu You, and YueXian Zou. 2021. Semantic transportation prototypical network for few-shot intent detection. In INTER-SPEECH.
Linfeng Yang, Rajarshi P Ghosh, J Matthew Franklin, Simon Chen, Chenyu You, Raja R Narayan, Marc L Melcher, and Jan T Liphardt. 2020. Nuset: A deep learning tool for reliably separating and analyzing crowded cells. PLoS computational biology.
Zhilin Yang, Zihang Dai, Yiming Yang, Jaime Carbonell, Russ R Salakhutdinov, and Quoc V Le. 2019. Xlnet: Generalized autoregressive pretraining for language understanding. In Advances in neural information processing systems (NeurIPS).
Chenyu You, Nuo Chen, Fenglin Liu, Dongchao Yang, and Yuexian Zou. 2020a. Towards data distillation for end-to-end spoken conversational question answering. arXiv preprint arXiv:2010.08923.
Chenyu You, Nuo Chen, and Yuexian Zou. 2021a. Contextualized attention-based knowledge transfer for spoken conversational question answering. In IN-TERSPEECH.
Chenyu You, Nuo Chen, and Yuexian Zou. 2021b. Knowledge distillation for improved accuracy in spoken question answering. In International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE.
Chenyu You, Nuo Chen, and Yuexian Zou. 2021c. MRD-Net: Multi-Modal Residual Knowledge Distillation for Spoken Question Answering. In *IJCAI*.
Chenyu You, Nuo Chen, and Yuexian Zou. 2021d. Self-supervised contrastive cross-modality representation learning for spoken question answering. In *Findings of the Association for Computational Linguistics: EMNLP*.
Chenyu You, Guang Li, Yi Zhang, Xiaoliu Zhang, Hongming Shan, Mengzhou Li, Shenghong Ju, Zhen Zhao, Zhuiyang Zhang, Wenxiang Cong, et al. 2019a. CT