Question: How do previous methods perform on the Switchboard Dialogue Act and DailyDialog datasets?
Evidence: We evaluate the performance of our model on two high-quality datasets: Switchboard Dialogue Act Corpus (SwDA) BIBREF4 and DailyDialog BIBREF24. SwDA has been widely used in previous work for the DA recognition task. It is annotated on 1155 human to human telephonic conversations about the given topic. Each utterance in the conversation is manually labeled as one of 42 dialogue acts according to SWBD-DAMSL taxonomy BIBREF25. In BIBREF10, they used 43 categories of dialogue acts, which is different from us and previous work. The difference in the number of labels is mainly due to the special label “+”, which represents that the utterance is interrupted by the other speaker (and thus split into two or more parts). We used the same processing with BIBREF26, which concatenated the parts of an interrupted utterance together, giving the result the tag of the first part and putting it in its place in the conversation sequence. It is critical for fair comparison because there are nearly 8% data has the label “+”. Lacking standard splits, we followed the training/validation/test splits by BIBREF14. DailyDialog dataset contains 13118 multi-turn dialogues, which mainly reflect our daily communication style. It covers various topics about our daily life. Each utterance in the conversation is manually labeled as one out of 4 dialogue act classes. Table TABREF18 presents the statistics for both datasets. In our preprocessing, the text was lowercased before tokenized, and then sentences were tokenized by WordPiece tokenizer BIBREF27 with a 30,000 token vocabulary to alleviate the Out-of-Vocabulary problem.
In this section, we evaluate the proposed approaches on SwDA dataset. Table TABREF20 shows our experimental results and the previous ones on SwDA dataset. It is worth noting that BIBREF10 combined GloVeBIBREF28 and pre-trained ELMo representationsBIBREF29 as word embeddings. However, in our work, we only applied the pre-trained word embedding. To illustrate the importance of context information, we also evaluate several sentence classification methods (CNN, LSTM, BERT) as baselines. For baseline models, both CNN and LSTM, got similar accuracy (75.27% and 75.59% respectively). We also fine-tuned BERT BIBREF30 to do recognition based on single utterance. As seen, with the powerful unsupervised pre-trained language model, BERT (76.88% accuracy) outperformed LSTM and CNN models for single sentence classification. However, it was still much lower than the models based on context information. It indicates that context information is crucial in the DA recognition task. BERT can boost performance in a large margin. However, it costs too much time and resources. In this reason, we chose LSTM as our utterance encoder in further experiment.
FLOAT SELECTED: Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset.
FLOAT SELECTED: Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing.
FLOAT SELECTED: Table 6: Experiment results on DailyDialog dataset.
To further illustrate the effect of the context length, we also performed experiments with different sliding window $W$ and context padding $P$. Table TABREF22 shows the result. It is worth noting that it is actually the same as single sentence classification when $P = 0$ (without any context provided). First, we set $W$ to 1 to discuss how the length of context padding will affect. As seen in the result, the accuracy increased when more context padding was used for both LSTM+BLSTM and LSTM+Attention approaches, so we did not evaluate the performance of LSTM+LC Attention when context padding is small. There was no further accuracy improvement when the length of context padding was beyond 5. Therefore, we fixed the context padding length $P$ to 5 and increased the size of the sliding window to see how it works. With sliding window size increasing, the more context was involved together with more unnecessary information. From the experiments, we can see that both LSTM+BLSTM and LSTM+Attention achieved the best performance when window size was 1 and context padding length was 5. When window size increased, the performances of these two models dropped. However, our model (LSTM+LC Attention) can leverage the context information more efficiently, which achieved the best performance when window size was 10, and the model was more stable and robust to the different setting of window size.
The classification accuracy of DailyDialog dataset is summarized in Table TABREF23. As for sentence classification without context information, the fine-tuned BERT still outperformed LSTM and CNN based models. From table TABREF18 we can see that, the average dialogue length $|U|$ in DailyDialog is much shorter than the average length of SwDA. So, in our experiment, we set the maximum of the $W$ to 10, which almost covers the whole utterances in the dialogue. Using the same way as SwDA dataset, we, first, set W to 1 and increased the length of context padding. As seen, modeling local context information, hierarchical models yielded significant improvement than sentence classification. There was no further accuracy improvement when the length of context padding was beyond 2, so we fixed the context padding length P to 2 and increased the size of sliding window size W. From the experiments, we can see that LSTM+Attention always got a little better accuracy than LSTM+BLSTM. With window size increasing, the performances of these two models dropped. Relying on modeling local contextual information, LSTM+LC Attention achieved the best accuracy (85.81%) when the window size was 5. For the longer sliding window, the performance of LSTM+LC Attention was still better and more robust than the other two models. For online prediction, we added 2 preceding utterances as context padding, and the experiment shows that LSTM+LC Attention outperformed the other two models under the online setting, although the performances of these three models dropped without subsequent utterances.
We evaluate the performance of our model on two high-quality datasets: Switchboard Dialogue Act Corpus (SwDA) BIBREF4 and DailyDialog BIBREF24. 
In this section, we evaluate the proposed approaches on SwDA dataset. Table TABREF20 shows our experimental results and the previous ones on SwDA dataset. 
FLOAT SELECTED: Table 4: Comparison results with the previous approaches and our approaches on SwDA dataset.
FLOAT SELECTED: Table 5: Experiment results about the hyperparameter W and P on SwDA dataset and online prediction result. W,P indicate the size of sliding window and context padding length during training and testing.
FLOAT SELECTED: Table 6: Experiment results on DailyDialog dataset.
To further illustrate the effect of the context length, we also performed experiments with different sliding window $W$ and context padding $P$. Table TABREF22 shows the result
The classification accuracy of DailyDialog dataset is summarized in Table TABREF23. As for sentence classification without context information, the fine-tuned BERT still outperformed LSTM and CNN based models.
Answer: The answers are shown as follows:
* Table TABREF20 
* Table TABREF22
* Table TABREF23