Analysing cross-lingual transfer in lemmatisation for Indian languages

Proceedings of the 28th International Conference on Computational Linguistics, pages 616–626 Barcelona, Spain (Online), December 8-13, 2020 616 Modeling Local Contexts for Joint Dialogue Act Recognition and Sentiment Classification with Bi-channel Dynamic Convolutions Jingye Li†, Hao Fei†, Donghong Ji‡ Key Laboratory of Aerospace Information Security and Trusted Computing, Ministry of Education, School of Cyber Science and Engineering, Wuhan University,Wuhan, China {theodorelee,hao.fei,dhji}@whu.edu.cn Abstract In this paper, we target improving the joint dialogue act recognition (DAR) and sentiment clas- sification (SC) tasks by fully modeling the local contexts of utterances. First, we employ the dynamic convolution network (DCN) as the utterance encoder to capture the dialogue contexts. Further, we propose a novel context-aware dynamic convolution network (CDCN) to better lever- age the local contexts when dynamically generating kernels. We extended our frameworks into bi-channel version (i.e., BDCN and BCDCN) under multi-task learning to achieve the joint DAR and SC. Two channels can learn their own feature representations for DAR and SC, respectively, but with latent interaction. Besides, we suggest enhancing the tasks by employing the DiaBERT language model. Our frameworks1 obtain state-of-the-art performances against all baselines on two benchmark datasets, demonstrating the importance of modeling the local contexts. 1 Intorduction Dialogue act recognition (DAR) aims to detect speaker’s intentions (e.g., question, agreement or state- ment) in each utterance, which can facilitate dialog systems to produce appropriate responses (Inui et al., 2001). Recent studies have further revealed that simultaneously recognizing the dialog act and detecting the sentiment in dialog can result in better grasping of speaker’s intention (Cerisara et al., 2018; Qin et al., 2020). These two tasks are closely relevant, i.e., they mutually promote each other by being jointly performed. On the one hand, the DAR provides clues for sentiment classification (SC). In return, the sentiment transitions also can benefit dialogue act prediction. Taking the utterances in Table 1 as exam- ples, it is quite common that a same sentiment following previous utterance’s will be expressed once the dialogue act Agreement is assigned. Meanwhile, when the speaker changes the sentiment from Negative to Neutral, the dialogues act tends to transition into Statement. Speaker Utterance Dialogue Act Sentiment A Does anyone ever feel anxious and empty at the same time? Question Negative B All the time. Answer Negative B I feel like I’m losing my mind a little bit. Statement Negative A Relatable. I’m always anxious and if I’m not feeling empty or depressed I’m angry. Also usually dissociating. Aggrement Negative B I needa go smoke. Statement Neutral Table 1: Example utterances from Mastodon dataset for joint dialogue act and sentiment detection. Prior works model the joint DAR and SC as sequence labeling problem, all accomplishing with recurrent-like neural models, e.g., Long Short-Term Memory Network (LSTM) (Chen et al., 2018; Ra- heja and Tetreault, 2019). However, one crucial drawback in these models is failing to fully incorporate 1Codes are publicly available at https://github.com/ljynlp/BCDCN. †Equally Contributed. ‡Corresponding author. This work is licensed under a Creative Commons Attribution 4.0 International License. License details: http:// creativecommons.org/licenses/by/4.0/. …

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