fastNLP/reproduction/matching

• Matching任务模型复现
• 数据集及复现结果汇总
• 数据集复现结果及其他主要模型对比
• • SNLI
  • • 基于fastNLP复现的结果
  • MNLI
  • • 基于fastNLP复现的结果
  • RTE
  • QNLI
  • • From GLUE baselines
    • • LSTM-based
      • Transformer-based
    • 基于fastNLP复现的结果
  • Quora

Matching任务模型复现

这里使用fastNLP复现了几个著名的Matching任务的模型，旨在达到与论文中相符的性能。这几个任务的评价指标均为准确率(%).

复现的模型有（按论文发表时间顺序排序）:

• CNTN：模型代码; 训练代码.
  论文链接：Convolutional Neural Tensor Network Architecture for Community-based Question Answering.
• ESIM：模型代码; 训练代码.
  论文链接：Enhanced LSTM for Natural Language Inference.
• DIIN：模型代码(still in progress); 训练代码(still in progress).
  论文链接：Natural Language Inference over Interaction Space.
• MwAN：模型代码; 训练代码.
  论文链接：Multiway Attention Networks for Modeling Sentence Pairs.
• BERT：模型代码; 训练代码.
  论文链接：BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding.

数据集及复现结果汇总

使用fastNLP复现的结果vs论文汇报结果，在前面的表示使用fastNLP复现的结果

'-'表示我们仍未复现或者论文原文没有汇报

model name	SNLI	MNLI	RTE	QNLI	Quora
CNTN 代码; 论文	77.79 vs -	63.29/63.16(dev) vs -	57.04(dev) vs -	62.38(dev) vs -	-
ESIM代码; 论文	88.13(glove) vs 88.0(glove)/88.7(elmo)	77.78/76.49 vs 72.4/72.1*	59.21(dev) vs -	76.97(dev) vs -	-
DIIN ; 论文	- vs 88.0	- vs 78.8/77.8	-	-	- vs 89.06
MwAN 代码; 论文	87.9 vs 88.3	77.3/76.7(dev) vs 78.5/77.7	-	74.6(dev) vs -	85.6 vs 89.12
BERT (BASE version)代码; 论文	90.6 vs -	- vs 84.6/83.4	67.87(dev) vs 66.4	90.97(dev) vs 90.5	-

*ESIM模型由MNLI官方复现的结果为72.4/72.1，ESIM原论文当中没有汇报MNLI数据集的结果。

数据集复现结果及其他主要模型对比

SNLI

Link to SNLI leaderboard

Performance on Test set:

model name	ESIM	DIIN	MwAN	GPT1.0	BERT-Large+SRL	MT-DNN
performance	88.0	88.0	88.3	89.9	91.3	91.6

基于fastNLP复现的结果

Performance on Test set:

model name	CNTN	ESIM	DIIN	MwAN	BERT-Base	BERT-Large
performance	77.79	88.13	-	87.9	90.6	91.16

MNLI

Link to MNLI main page

Performance on Test set(matched/mismatched):

model name	ESIM	DIIN	MwAN	GPT1.0	BERT-Base	MT-DNN
performance	72.4/72.1	78.8/77.8	78.5/77.7	82.1/81.4	84.6/83.4	87.9/87.4

基于fastNLP复现的结果

Performance on Test set(matched/mismatched):

model name	CNTN	ESIM	DIIN	MwAN	BERT-Base
performance	63.29/63.16(dev)	77.78/76.49	-	77.3/76.7(dev)	-

RTE

Still in progress.

QNLI

From GLUE baselines

Link to GLUE leaderboard

Performance on Test set:

LSTM-based

model name	BiLSTM	BiLSTM + Attn	BiLSTM + ELMo	BiLSTM + Attn + ELMo
performance	74.6	74.3	75.5	79.8

*这些LSTM-based的baseline是由QNLI官方实现并测试的。

Transformer-based

model name	GPT1.0	BERT-Base	BERT-Large	MT-DNN
performance	87.4	90.5	92.7	96.0

基于fastNLP复现的结果

Performance on Dev set:

model name	CNTN	ESIM	DIIN	MwAN	BERT
performance	62.38	76.97	-	74.6	-

Quora

Still in progress.