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DialogueEIN

Introduction

This is a reproduction on DialogueEIN: Emotion Interaction Network for Dialogue Affective Analysis-COLING22.

Project structure refers to @shenwzh3/DAG-ERC. Features and Dataset can be found there.

Model

image-20221111190813432

Experiment Result

IEMOCAP MELD
Ref-weighted-avg-f-score 68.93 65.37
result-v1 64.22 62.65
result-SSC 65.67 63.59
result-v2 62.83 63.3
result-v3 63.38 63.03
result-v4 63.84 63.1
result-v5 64.6 64.52
result-v6 63.56 65.11

explanation

  • V1: the inputs of the encoder in the semantic interaction network are featrues have been extracted by roberta-large in advance, fixed.

  • SSC: remove DialogueEIN structure, substitute with a three layers MLP, hidden size the same with BERT config.hidden_size.

  • V2: nearly the same as DialogueEIN raised in original paper, except separate learning rate and the linear transform layer after roberta.

  • V3: add linear transform compared to v2

  • V4: add separate learning rate compared to v3

  • V5: add linear learning rate decay compared to v4

  • V6: fix a fatal bug: extended_mask should be -10000 or 0

Some Validation Experiments

Acc F1-score-avg
DAG-v2 91.13 91.66
DialogueEIN-roberta-large 91.68 89.47
DialogueEIN-feature-fixed 95.52 95.71

These experiments trained on JDDC dataset. Params setting is nearly the same as original paper(bsz=8, lr settings are exactly two groups refered in paper, local_att_window_size=7)

Expr1 and Expr3 are feature fixed(using features extracted from chinese-roberta-base-wwm) and all use last four layers cls as utterance feature.

feature-fixed method boost the model performance showing my reproduction is relatively believable.

Some Ablation Experiments

Model\Metric Acc f-score-weighted
w/o local 91.41 92.3
w/o intra 96.44 96.48
w/o inter 95.8 95.65
w/o global 94.24 94.08
w/o emotion-ebd 88.21 89.41
w/o emotion-interaction 89.03 89.67
DialogueEIN-feature-fixed-share 93.42 93.96
separate-emo-embedding 95.34 95.56
w/o residual 93.05 93.5
encoder_layer = 2 94.06 94.43

Some model structure modification and ablation, which are implemented on JDDC Chinese Dataset.

Some annoying bugs

on the reproduction on MELD, I wrongly choose the 12th layer cls repr as the utterance feature using roberta-large, so the result-v4 on MELD is little worse than original paper.

the training process on IEMOCAP isn't that satisfactory...