FERN et: Fine-grained Extraction and Reasoning Network for Emotion Recognition in Dialogues (2020)

Abstract: Unlike non-conversation scenes, emotion recognition in dialogues (ERD) poses more complicated challenges due to its interactive nature and intricate contextual information. All present methods model historical utterances without considering the content of the target utterance. However, different parts of a historical utterance may contribute differently to emotion inference of different target utterances. Therefore we propose Fine-grained Extraction and Reasoning Network (FERNet) to generate target-specific historical utterance representations. The reasoning module effectively handles both local and global sequential dependencies to reason over context, and updates target utterance representations to more informed vectors. Experiments on two benchmarks show that our method achieves competitive performance compared with previous methods.

## introduction with the development of human-machine interaction (hmi) applications, textual dialogue scenes appear more frequently. these scenes request effective and high-performance emotion recognition systems helping in building empathetic machines @xcite . therefore, emotion recognition in dialogues (erd) is getting growing attention from both academic and business community. different from non-conversation scenes, the erd task poses a more complicated challenge of modeling context-sensitive dependencies. most of existing approaches adopt convolution neural network (cnn) @xcite , followed by a max-pooling layer to obtain utterance representations @xcite @xcite @xcite . the process proceeds without the guidance of the target utterance, thus generated historical utterance representations are indistinguishable toward different target utterances. emotion recognition may fail in cases where historical utterances express various emotions toward various targets, which may confuse the emotion recognition of target utterances. as figure 1 shows, for different target utterances b 1 and b 2 , the model should attend the words "good service" and "bad food" in a 1 , separately. in a word, it is desired to pay different attention to different words of a certain historical utterance to generate the target-specific historical utterance representation. the restaurant where we had dinner provided [good service] but [bad food]. ## related work primitive approaches deal with the erd task as simple solely-sentence emotion recognition task with no consideration of the historical information @xcite @xcite . to exploit contextual information, @xcite al. (2017); huang et al. (2019); jiao et al. (2019); hazarika et al. (2018a,b); torres (2018 some of these works consider the context following the target utterance such as @xcite ; @xcite ; @xcite and some variants of @xcite . however, this condition is quite incompatible with some practical situations like real-time dialogue systems in which we possess no future utterances while handling the target utterance. so in our paper, we only focus on the setting that only historical utterances can be utilized. ## proposed model each dialogue d consists of two parts denoted as @xmath0, s)}, where @xmath1, u 2 , ..., u n ] is a sequence of utterances ordered based on their temporal occurrence. @xmath2, s 2 , ..., s n ] denotes corresponding speakers and n is the number of utterances in the dialogue. the erd task aims to predict @xmath3, y 2 , ..., y n ], where y @xmath4. c is the set of candidate emotion categories. the fernet consists of four successive modules: feature extraction module, attention module, reasoning module and output module. figure 2 presents the overall architecture of the proposed model. ## conclusion in this paper, we propose fernet to solve the erd task. the model generates target-specific historical utterances according to the content of the target utterance using attention mechanism. the reasoning module effectively handles both local and global sequential dependencies to update the original representation of the target utterance to a more informed vector. our model achieves competitive performance on two benchmarks. training details we use 10% of the training set as the validation set for hyper-parameters tuning. all tokens are lowercased with removal of stop words, symbols and digits, and sentences are zero-padded to the length of the longest sentence in the dataset. we alter the weight that each training instance carries when computing the loss to mitigate the influence of data imbalance.