MRC Ablation

This is a repository for the paper "Assessing the Benchmarking Capacity of Machine Reading Comprehension Datasets" (Sugawara et al., AAAI 2020).

Analyzed Datasets

	Dataset	year	web	spec	size	paper	misc
1	CoQA	2018	link	dialogue-based QA	127k	link
2	DuoRC	2018	link	QA on movie scripts	186k	link
3	HotpotQA	2018	link	multi-hop reasoning	113k	link
4	SQuAD1.1	2016	link	QA on Wikipedia	100k	link
5	SQuAD2.0	2018	link	unanswerable QA on Wikipedia	100k	link
6	ARC	2018	link	science exam on retrieved docs	8k	link
7	MCTest	2015	link	children-level narrative QA	2.6k	link
8	MultiRC	2018	link	multi-sentence QA	6k	link
9	RACE	2017	link	English exam	100k	link
10	SWAG	2018	link	machine-generated commonsense QA	113k	link

Ablation Methods

Each dataset directory under results contains following directories:

	Ablation method	Directory	Description
0	original	original	the original data (development set)
1	Question interrogatives only	drop_question_except_interrogatives	drop question words except interrogatives (wh*, how)
2	Function words only	drop_content_words	drop content words (verb, noun, ...)
3	Content words only	drop_function_words	drop function words (= stop words here)
4	Vocabulary anonymization	vocab_anon	replace tokens with their POS tags
5	Question-context similarity	drop_except_most_similar_sentences	keep the sentences that are the most similar to the question in terms of unigram overlap and drop the other sentences.
6	Shuffle context words	shuffle_document_words	randomly shuffle all words in the context
7	Shuffle sentence words	shuffle_sentence_words	randomly shuffle the words in all the sentences except the last token
8	Shuffle sentence order	shuffle_sentence_order	randomly shuffle the order of the sentences in the context
9	Dummy numerics	mask_numerics	replace numerical expressions with random numbers
10	Logical words dropped	drop_logical_words	drop logical terms such as not, every, and if
11	Pronoun words dropped	mask_pronouns	drop personal and possessive pronouns (PRP and PRP$ tags)
12	Causal words dropped	drop_causal_words	drop causal terms/clauses such as because and therefore
3'	(trained) content words only	train_content_only	drop function words (= stop words here) (also in training)
6'	(trained) shuffle context words	train_doc_shuff	randomly shuffle all words in the context (also in training)
7'	(trained) shuffle sentence words	train_sent_shuff	randomly shuffle the words in all the sentences except the last token (also in training)
x	Context dropped	drop_question_words	drop all question words
y	Question dropped	drop_context_words	drop all context words
z	Options only	drop_except_options	drop all question and context words (only for multiple choice datasets)

There are results of five different seeds for the shuffle-based methods (seed1 to seed12345).

Each result directory has args_log.txt that specifies hyperparameters.