Question: Does the RL model create responses that are potentially better at mitigating hate speech and are more diverse?
Evidence: The experimental results of the detection task and the generative intervention task are shown in Table TABREF27 and Table TABREF29 separately. The results of the human evaluation are shown in Table TABREF30. Figure FIGREF25 shows examples of the generated responses.
As shown in Table TABREF27 and TABREF29, all the classification and generative models perform better on the Gab dataset than on the Reddit dataset. We think this stems from the datasets' characteristics. First, the Gab dataset is larger and has a more balanced category distribution than the Reddit dataset. Therefore, it is inherently more challenging to train a classifier on the Reddit dataset. Further, the average lengths of the Reddit posts and conversations are much larger than those of Gab, potentially making the Reddit input nosier than the Gab input for both tasks. On both the Gab and Reddit datasets, the SVM classifier and the LR classifier achieved better performance than the CNN and RNN model with randomly initialized word embeddings. A possible reason is that without pretrained word embeddings, the neural network models tend to overfit on the dataset.
For the generative intervention task, three models perform similarly on all three automatic evaluation metrics. As expected, the Seq2Seq model achieves higher scores with filtered conversation as input. However, this is not the case for the VAE model. This indicates that the two models may have different capabilities to capture important information in conversations.
As shown in Table TABREF29, applying Reinforcement Learning does not lead to higher scores on the three automatic metrics. However, human evaluation (Table TABREF30) shows that the RL model creates responses that are potentially better at mitigating hate speech and are more diverse, which is consistent with BIBREF21. There is a larger performance difference with the Gab dataset, while the effectiveness and the diversity of the responses generated by the Seq2Seq model and the RL model are quite similar on the Reddit dataset. One possible reason is that the size of the training data from Reddit (around 8k) is only 30% the size of the training data from Gab. The inconsistency between the human evaluation results and the automatic ones indicates the automatic evaluation metrics listed in Table TABREF29 can hardly reflect the quality of the generated responses. As mentioned in Section SECREF4, annotators tend to have strategies for intervention. Therefore, generating the common parts of the most popular strategies for all the testing input can lead to high scores of these automatic evaluation metrics. For example, generating “Please do not use derogatory language.” for all the testing Gab data can achieve 4.2 on BLEU, 20.4 on ROUGE, and 18.2 on METEOR. However, this response is not considered as high-quality because it is almost a universal response to all the hate speech, regardless of the context and topic.
Surprisingly, the responses generated by the VAE model have much worse diversity than the other two methods according to human evaluation. As indicated in Figure FIGREF25, the responses generated by VAE tend to repeat the responses related to some popular hate keyword. For example, “Use of the r-word is unacceptable in our discourse as it demeans and insults people with mental disabilities.” and “Please do not use derogatory language for intellectual disabilities.” are the generated responses for a large part of the Gab testing data. According to Figure FIGREF20, insults towards disabilities are the largest portion in the dataset, so we suspect that the performance of the VAE model is affected by the imbalanced keyword distribution.
The sampled results in Figure FIGREF25 show that the Seq2Seq and the RL model can generate reasonable responses for intervention. However, as is to be expected with machine-generated text, in the other human evaluation we conducted, where Mechanical Turk workers were also presented with sampled human-written responses alongside the machine generated responses, the human-written responses were chosen as the most effective and diverse option a majority of the time (70% or more) for both datasets. This indicates that there is significant room for improvement while generating automated intervention responses.
In our experiments, we only utilized the text of the posts, but more information is available and can be utilized, such as the user information and the title of a Reddit submission.
However, human evaluation (Table 4) shows that the RL model creates responses that are potentially better at mitigating hate speech and are more diverse, which is consistent with Li et al. (2016).
Answer: Yes, it does.