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Dataset Question Answering on SQuAD1.1 dev

Question Answering on SQuAD1.1 dev

#
MODEL
REPOSITORY
EM F1 SPEED
PAPER
ε-REPRODUCES PAPER
1
SpanBERT
facebookresearch / SpanBERT
88.1% 94.0% 28.8
--
2
BERT large
(whole word masking, uncased)
huggingface / transformers
86.9% 93.2%
--
3
BERT large
(whole word masking, cased)
huggingface / transformers
86.3% 92.8%
--
4
DistilBERT
huggingface / transformers
79.1% 86.9%
5
BIDAF
(single)
allenai / allennlp
68.4% 77.8% 205.6
Models on Papers with Code for which code has not been tried out yet.
MODEL
PAPER
EM F1
PWC RANK
T5-11B
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
90.1% 95.6%
1
XLNet
(no data aug)
XLNet: Generalized Autoregressive Pretraining for Language Understanding
88.9% 94.5%
2
T5-3B
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
88.5% 95.0%
3
T5-Large
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
86.7% 93.8%
4
T5-Base
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
85.4% 92.1%
5
BERT large
(+TriviaQA)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
84.2% 91.1%
6
BERT large
(single)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
84.1% 90.9%
7
BERT base
(single)
BERT: Pre-training of Deep Bidirectional Transformers for Language Understanding
80.8% 88.5%
8
T5-Small
Exploring the Limits of Transfer Learning with a Unified Text-to-Text Transformer
79.1% 87.2%
9
R.M-Reader
(single)
Reinforced Mnemonic Reader for Machine Reading Comprehension
78.9% 86.3%
10
KAR
Explicit Utilization of General Knowledge in Machine Reading Comprehension
76.7% 84.9%
12
SAN
(single)
Stochastic Answer Networks for Machine Reading Comprehension
76.2% 84.1%
13
FusionNet
FusionNet: Fusing via Fully-Aware Attention with Application to Machine Comprehension
75.3% 83.6%
14
QANet
(data aug x3)
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
75.1% 83.8%
15
QANet
(data aug x2)
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
74.5% 83.2%
16
DCN+
(single)
DCN+: Mixed Objective and Deep Residual Coattention for Question Answering
74.5% 83.1%
17
QANet
QANet: Combining Local Convolution with Global Self-Attention for Reading Comprehension
73.6% 82.7%
18
PhaseCond
(single)
Phase Conductor on Multi-layered Attentions for Machine Comprehension
72.1% 81.4%
19
SRU
Simple Recurrent Units for Highly Parallelizable Recurrence
71.4% 80.2%
20
Smarnet
Smarnet: Teaching Machines to Read and Comprehend Like Human
71.4% 80.2%
21
DCN
(Char + CoVe)
Learned in Translation: Contextualized Word Vectors
71.3% 79.9%
22
R-NET
(single)
Gated Self-Matching Networks for Reading Comprehension and Question Answering
71.1% 79.5%
23
Ruminating Reader
Ruminating Reader: Reasoning with Gated Multi-Hop Attention
70.6% 79.5%
24
FastQAExt
(beam-size 5)
Making Neural QA as Simple as Possible but not Simpler
70.3% 78.5%
25
DrQA
(Document Reader only)
Reading Wikipedia to Answer Open-Domain Questions
69.5% 78.8%
26
jNet
(TreeLSTM adaptation, QTLa, K=…)
Exploring Question Understanding and Adaptation in Neural-Network-Based Question Answering
69.1% 78.4%
27
SEDT-LSTM
Structural Embedding of Syntactic Trees for Machine Comprehension
67.9% 77.4%
28
SECT-LSTM
Structural Embedding of Syntactic Trees for Machine Comprehension
67.7% 77.2%
30
RASOR
Learning Recurrent Span Representations for Extractive Question Answering
66.4% 74.9%
31
MPCM
Multi-Perspective Context Matching for Machine Comprehension
66.1% 75.8%
32
DCN
Dynamic Coattention Networks For Question Answering
65.4% 75.6%
33
FABIR
A Fully Attention-Based Information Retriever
65.1% 75.6%
34
Match-LSTM with Bi-Ans-Ptr
(Boundary+Search+b)
Machine Comprehension Using Match-LSTM and Answer Pointer
64.1% 64.7%
35
OTF dict+spelling
(single)
Learning to Compute Word Embeddings On the Fly
63.1%
36
DCR
End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension
62.5% 71.2%
37
FG fine-grained gate
Words or Characters? Fine-grained Gating for Reading Comprehension
60.0% 71.2%
38

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