Datasets:

seantrott
/

rawc

word stringclasses 112 values	sentence1 stringlengths 14 37	sentence2 stringlengths 14 36	same bool 2 classes	ambiguity_type stringclasses 2 values	disambiguating_word1 stringlengths 2 15	disambiguating_word2 stringlengths 2 15	version stringclasses 6 values	Class stringclasses 2 values	mean_relatedness float64 0 4	median_relatedness float64 0 4	diff float64 0 1.47	count int64 4 23	sd_relatedness float64 0 2	se_relatedness float64 0 0.71	v1 stringclasses 3 values	v2 stringclasses 3 values	dominance_sentence2 float64 -1.78 1.71 ⌀	sd_dominance_sentence2 float64 0.43 1.86 ⌀	string stringclasses 112 values
act	It was a desperate act.	It was a magic act.	false	Polysemy	desperate	magic	M1_a_M2_a	N	2.181818	2	0.181818	11	1.32802	0.400413	M1_a	M2_a	-0.533333	1.187234	act
act	It was a desperate act.	It was a comedic act.	false	Polysemy	desperate	comedic	M1_a_M2_b	N	2	2	0	7	1.290994	0.48795	M1_a	M2_b	0.176471	1.467791	act
act	It was a humane act.	It was a magic act.	false	Polysemy	humane	magic	M1_b_M2_a	N	2.818182	3	0.181818	11	0.98165	0.295979	M1_b	M2_a	0.103448	1.519982	act
act	It was a humane act.	It was a comedic act.	false	Polysemy	humane	comedic	M1_b_M2_b	N	2.809524	3	0.190476	21	0.928388	0.202591	M1_b	M2_b	-0.411765	1.543487	act
act	It was a desperate act.	It was a humane act.	true	Polysemy	desperate	humane	M1_a_M1_b	N	3.9	4	0.1	10	0.316228	0.1	M1_a	M1_b	null	null	act
act	It was a magic act.	It was a comedic act.	true	Polysemy	magic	comedic	M2_a_M2_b	N	3.764706	4	0.235294	17	0.437237	0.106046	M2_a	M2_b	null	null	act
appeal	He had a universal appeal.	He had a legal appeal.	false	Polysemy	universal	legal	M1_a_M2_a	N	1.571429	1	0.571429	14	1.741542	0.465447	M1_a	M2_a	0.117647	1.409005	appeal
appeal	He had a universal appeal.	He had a pending appeal.	false	Polysemy	universal	pending	M1_a_M2_b	N	1	0	1	18	1.328422	0.313112	M1_a	M2_b	-0.352941	1.729927	appeal
appeal	He had an undefinable appeal.	He had a legal appeal.	false	Polysemy	undefinable	legal	M1_b_M2_a	N	1.235294	1	0.235294	17	1.20049	0.291162	M1_b	M2_a	0.08	1.552417	appeal
appeal	He had an undefinable appeal.	He had a pending appeal.	false	Polysemy	undefinable	pending	M1_b_M2_b	N	0.714286	0	0.714286	7	1.253566	0.473804	M1_b	M2_b	0.315789	1.454977	appeal
appeal	He had a universal appeal.	He had an undefinable appeal.	true	Polysemy	universal	undefinable	M1_a_M1_b	N	3.7	4	0.3	10	0.483046	0.152753	M1_a	M1_b	null	null	appeal
appeal	He had a legal appeal.	He had a pending appeal.	true	Polysemy	legal	pending	M2_a_M2_b	N	3.272727	4	0.727273	11	0.904534	0.272727	M2_a	M2_b	null	null	appeal
atmosphere	It was a tense atmosphere.	It was a gaseous atmosphere.	false	Polysemy	tense	gaseous	M1_a_M2_a	N	1.214286	1	0.214286	14	1.050902	0.280865	M1_a	M2_a	-0.64	1.319091	atmosphere
atmosphere	It was a tense atmosphere.	It was a polluted atmosphere.	false	Polysemy	tense	polluted	M1_a_M2_b	N	1.846154	2	0.153846	13	1.281025	0.355292	M1_a	M2_b	0.6	1.404076	atmosphere
atmosphere	It was a hostile atmosphere.	It was a gaseous atmosphere.	false	Polysemy	hostile	gaseous	M1_b_M2_a	N	1.538462	2	0.461538	13	0.877058	0.243252	M1_b	M2_a	-0.64	1.410674	atmosphere
atmosphere	It was a hostile atmosphere.	It was a polluted atmosphere.	false	Polysemy	hostile	polluted	M1_b_M2_b	N	2.181818	2	0.181818	11	0.873863	0.26348	M1_b	M2_b	0.307692	1.652504	atmosphere
atmosphere	It was a tense atmosphere.	It was a hostile atmosphere.	true	Polysemy	tense	hostile	M1_a_M1_b	N	4	4	0	11	0	0	M1_a	M1_b	null	null	atmosphere
atmosphere	It was a gaseous atmosphere.	It was a polluted atmosphere.	true	Polysemy	gaseous	polluted	M2_a_M2_b	N	3.466667	4	0.533333	15	0.63994	0.165232	M2_a	M2_b	null	null	atmosphere
bail	He bailed out the prisoner.	He bailed out the water.	false	Homonymy	prisoner	water	M1_a_M2_a	V	0.894737	1	0.105263	19	1.100239	0.252412	M1_a	M2_a	-1.333333	0.816497	bailed
bail	He bailed out the prisoner.	He bailed out the liquid.	false	Homonymy	prisoner	liquid	M1_a_M2_b	V	0.666667	1	0.333333	9	0.707107	0.235702	M1_a	M2_b	-1.153846	1.433661	bailed
bail	He bailed out the defendant.	He bailed out the water.	false	Homonymy	defendant	water	M1_b_M2_a	V	0.75	0.5	0.25	8	0.886405	0.313392	M1_b	M2_a	-1.5	1.100239	bailed
bail	He bailed out the defendant.	He bailed out the liquid.	false	Homonymy	defendant	liquid	M1_b_M2_b	V	0.764706	1	0.235294	17	0.752447	0.182495	M1_b	M2_b	-1.647059	0.785905	bailed
bail	He bailed out the prisoner.	He bailed out the defendant.	true	Homonymy	prisoner	defendant	M1_a_M1_b	V	3.083333	4	0.916667	12	1.378954	0.39807	M1_a	M1_b	null	null	bailed
bail	He bailed out the water.	He bailed out the liquid.	true	Homonymy	water	liquid	M2_a_M2_b	V	3.083333	3.5	0.416667	12	1.083625	0.312815	M2_a	M2_b	null	null	bailed
band	It was a gold band.	It was a jazz band.	false	Homonymy	gold	jazz	M1_a_M2_a	N	0.2	0	0.2	10	0.421637	0.133333	M1_a	M2_a	0.8	1.373213	band
band	It was a gold band.	It was a country band.	false	Homonymy	gold	country	M1_a_M2_b	N	0.384615	0	0.384615	13	0.50637	0.140442	M1_a	M2_b	0.761905	1.445848	band
band	It was a rubber band.	It was a jazz band.	false	Homonymy	rubber	jazz	M1_b_M2_a	N	0.333333	0	0.333333	12	0.651339	0.188025	M1_b	M2_a	0.272727	1.453463	band
band	It was a rubber band.	It was a country band.	false	Homonymy	rubber	country	M1_b_M2_b	N	0.1	0	0.1	10	0.316228	0.1	M1_b	M2_b	-0.5	1.538968	band
band	It was a gold band.	It was a rubber band.	true	Homonymy	gold	rubber	M1_a_M1_b	N	2.526316	3	0.473684	19	1.218762	0.279603	M1_a	M1_b	null	null	band
band	It was a jazz band.	It was a country band.	true	Homonymy	jazz	country	M2_a_M2_b	N	4	4	0	13	0	0	M2_a	M2_b	null	null	band
bank	He banked the plane.	He banked the money.	false	Homonymy	plane	money	M1_a_M2_a	V	1	1	0	17	1.06066	0.257248	M1_a	M2_a	0.25	1.551739	banked
bank	He banked the plane.	He banked the cash.	false	Homonymy	plane	cash	M1_a_M2_b	V	0.733333	1	0.266667	15	0.703732	0.181703	M1_a	M2_b	1.157895	1.213954	banked
bank	He banked the helicopter.	He banked the money.	false	Homonymy	helicopter	money	M1_b_M2_a	V	0.6	1	0.4	10	0.516398	0.163299	M1_b	M2_a	1.214286	1.050902	banked
bank	He banked the helicopter.	He banked the cash.	false	Homonymy	helicopter	cash	M1_b_M2_b	V	1.375	1.5	0.125	8	1.30247	0.460493	M1_b	M2_b	0.48	1.475353	banked
bank	He banked the plane.	He banked the helicopter.	true	Homonymy	plane	helicopter	M1_a_M1_b	V	3.333333	3.5	0.166667	12	0.778499	0.224733	M1_a	M1_b	null	null	banked
bank	He banked the money.	He banked the cash.	true	Homonymy	money	cash	M2_a_M2_b	V	3.533333	4	0.466667	15	0.833809	0.215289	M2_a	M2_b	null	null	banked
barrier	There was a police barrier.	There was a language barrier.	false	Polysemy	police	language	M1_a_M2_a	N	1.333333	1	0.333333	12	1.154701	0.333333	M1_a	M2_a	-0.047619	1.499206	barrier
barrier	There was a police barrier.	There was a religious barrier.	false	Polysemy	police	religious	M1_a_M2_b	N	1.266667	2	0.733333	15	1.032796	0.266667	M1_a	M2_b	-0.823529	0.951006	barrier
barrier	There was a concrete barrier.	There was a language barrier.	false	Polysemy	concrete	language	M1_b_M2_a	N	1.692308	2	0.307692	13	1.250641	0.346865	M1_b	M2_a	0.181818	1.367527	barrier
barrier	There was a concrete barrier.	There was a religious barrier.	false	Polysemy	concrete	religious	M1_b_M2_b	N	1.727273	2	0.272727	11	1.3484	0.406558	M1_b	M2_b	-1	1.328422	barrier
barrier	There was a police barrier.	There was a concrete barrier.	true	Polysemy	police	concrete	M1_a_M1_b	N	2.923077	3	0.076923	13	1.320451	0.366227	M1_a	M1_b	null	null	barrier
barrier	There was a language barrier.	There was a religious barrier.	true	Polysemy	language	religious	M2_a_M2_b	N	3.538462	4	0.461538	13	0.77625	0.215293	M2_a	M2_b	null	null	barrier
bat	He saw a furry bat.	He saw a wooden bat.	false	Homonymy	furry	wooden	M1_a_M2_a	N	0	0	0	10	0	0	M1_a	M2_a	0.52	1.417745	bat
bat	He saw a furry bat.	He saw a baseball bat.	false	Homonymy	furry	baseball	M1_a_M2_b	N	0.5	0	0.5	14	0.85485	0.228468	M1_a	M2_b	1.055556	0.872604	bat
bat	He saw a fruit bat.	He saw a wooden bat.	false	Homonymy	fruit	wooden	M1_b_M2_a	N	0.2	0	0.2	10	0.632456	0.2	M1_b	M2_a	1.052632	1.025978	bat
bat	He saw a fruit bat.	He saw a baseball bat.	false	Homonymy	fruit	baseball	M1_b_M2_b	N	0.083333	0	0.083333	12	0.288675	0.083333	M1_b	M2_b	0.8125	1.327592	bat
bat	He saw a furry bat.	He saw a fruit bat.	true	Homonymy	furry	fruit	M1_a_M1_b	N	3.55	4	0.45	20	0.998683	0.223312	M1_a	M1_b	null	null	bat
bat	He saw a wooden bat.	He saw a baseball bat.	true	Homonymy	wooden	baseball	M2_a_M2_b	N	3.545455	4	0.454545	11	1.21356	0.365902	M2_a	M2_b	null	null	bat
beam	He saw the laser beam.	He saw the wood beam.	false	Polysemy	laser	wood	M1_a_M2_a	N	1.142857	1	0.142857	14	0.949262	0.253701	M1_a	M2_a	-0.5625	1.314978	beam
beam	He saw the laser beam.	He saw the balance beam.	false	Polysemy	laser	balance	M1_a_M2_b	N	0.833333	1	0.166667	12	0.937437	0.270615	M1_a	M2_b	-0.705882	1.358524	beam
beam	He saw the bright beam.	He saw the wood beam.	false	Polysemy	bright	wood	M1_b_M2_a	N	0.555556	0	0.555556	9	1.333333	0.444444	M1_b	M2_a	-0.5	1.336306	beam
beam	He saw the bright beam.	He saw the balance beam.	false	Polysemy	bright	balance	M1_b_M2_b	N	0.363636	0	0.363636	11	0.6742	0.203279	M1_b	M2_b	-0.043478	1.460954	beam
beam	He saw the laser beam.	He saw the bright beam.	true	Polysemy	laser	bright	M1_a_M1_b	N	3.545455	4	0.454545	11	0.687552	0.207305	M1_a	M1_b	null	null	beam
beam	He saw the wood beam.	He saw the balance beam.	true	Polysemy	wood	balance	M2_a_M2_b	N	2.6	3	0.4	20	1.187656	0.265568	M2_a	M2_b	null	null	beam
block	She had a toy block.	She had a mental block.	false	Polysemy	toy	mental	M1_a_M2_a	N	0.538462	0	0.538462	13	0.967418	0.268313	M1_a	M2_a	-0.6	1.273206	block
block	She had a toy block.	She had a writer's block.	false	Polysemy	toy	writer	M1_a_M2_b	N	0.133333	0	0.133333	15	0.516398	0.133333	M1_a	M2_b	-0.8125	1.167262	block
block	She had a wooden block.	She had a mental block.	false	Polysemy	wooden	mental	M1_b_M2_a	N	1	1	0	13	1.224745	0.339683	M1_b	M2_a	-0.5	1.532262	block
block	She had a wooden block.	She had a writer's block.	false	Polysemy	wooden	writer	M1_b_M2_b	N	0.2	0	0.2	10	0.421637	0.133333	M1_b	M2_b	0.277778	1.48742	block
block	She had a toy block.	She had a wooden block.	true	Polysemy	toy	wooden	M1_a_M1_b	N	3.8	4	0.2	15	0.414039	0.106904	M1_a	M1_b	null	null	block
block	She had a mental block.	She had a writer's block.	true	Polysemy	mental	writer	M2_a_M2_b	N	3.181818	3	0.181818	11	0.98165	0.295979	M2_a	M2_b	null	null	block
blood	He has coagulated blood.	He has noble blood.	false	Polysemy	coagulated	noble	M1_a_M2_a	N	1.6	2	0.4	15	0.985611	0.254484	M1_a	M2_a	-0.789474	1.436777	blood
blood	He has coagulated blood.	He has royal blood.	false	Polysemy	coagulated	royal	M1_a_M2_b	N	2.5	2.5	0	8	0.92582	0.327327	M1_a	M2_b	-0.6875	1.400893	blood
blood	He has thin blood.	He has noble blood.	false	Polysemy	thin	noble	M1_b_M2_a	N	2.222222	2	0.222222	9	1.641476	0.547159	M1_b	M2_a	-0.25	1.51083	blood
blood	He has thin blood.	He has royal blood.	false	Polysemy	thin	royal	M1_b_M2_b	N	2.214286	2	0.214286	14	1.121714	0.299791	M1_b	M2_b	-0.210526	1.474937	blood
blood	He has coagulated blood.	He has thin blood.	true	Polysemy	coagulated	thin	M1_a_M1_b	N	3.631579	4	0.368421	19	0.955134	0.219123	M1_a	M1_b	null	null	blood
blood	He has noble blood.	He has royal blood.	true	Polysemy	noble	royal	M2_a_M2_b	N	3.583333	4	0.416667	12	0.792961	0.228908	M2_a	M2_b	null	null	blood
board	It was the ironing board.	It was the executive board.	false	Polysemy	ironing	executive	M1_a_M2_a	N	0.066667	0	0.066667	15	0.258199	0.066667	M1_a	M2_a	-0.428571	1.650841	board
board	It was the ironing board.	It was the school board.	false	Polysemy	ironing	school	M1_a_M2_b	N	0.416667	0	0.416667	12	0.996205	0.28758	M1_a	M2_b	0.611111	1.377931	board
board	It was the wooden board.	It was the executive board.	false	Polysemy	wooden	executive	M1_b_M2_a	N	0.25	0	0.25	12	0.452267	0.130558	M1_b	M2_a	0.190476	1.435933	board
board	It was the wooden board.	It was the school board.	false	Polysemy	wooden	school	M1_b_M2_b	N	0.6	0	0.6	15	0.736788	0.190238	M1_b	M2_b	-0.88	1.268858	board
board	It was the ironing board.	It was the wooden board.	true	Polysemy	ironing	wooden	M1_a_M1_b	N	2.625	2.5	0.125	8	1.30247	0.460493	M1_a	M1_b	null	null	board
board	It was the executive board.	It was the school board.	true	Polysemy	executive	school	M2_a_M2_b	N	3.666667	4	0.333333	15	0.617213	0.159364	M2_a	M2_b	null	null	board
book	He had a best-selling book.	He had a heavy book.	false	Polysemy	best-selling	heavy	M1_a_M2_a	N	3.692308	4	0.307692	13	1.1094	0.307692	M1_a	M2_a	-1.055556	1.21133	book
book	He had a best-selling book.	He had a leather-bound book.	false	Polysemy	best-selling	leather-bound	M1_a_M2_b	N	3.357143	4	0.642857	14	1.081818	0.289128	M1_a	M2_b	-0.764706	1.393261	book
book	He had a popular book.	He had a heavy book.	false	Polysemy	popular	heavy	M1_b_M2_a	N	4	4	0	11	0	0	M1_b	M2_a	-0.315789	1.454977	book
book	He had a popular book.	He had a leather-bound book.	false	Polysemy	popular	leather-bound	M1_b_M2_b	N	3.818182	4	0.181818	11	0.603023	0.181818	M1_b	M2_b	-1.166667	0.963087	book
book	He had a best-selling book.	He had a popular book.	true	Polysemy	best-selling	popular	M1_a_M1_b	N	4	4	0	12	0	0	M1_a	M1_b	null	null	book
book	He had a heavy book.	He had a leather-bound book.	true	Polysemy	heavy	leather-bound	M2_a_M2_b	N	3.75	4	0.25	16	0.68313	0.170783	M2_a	M2_b	null	null	book
break	He broke the glass.	He broke the law.	false	Polysemy	glass	law	M1_a_M2_a	V	1.733333	2	0.266667	15	1.032796	0.266667	M1_a	M2_a	-1.105263	1.196975	broke
break	He broke the glass.	He broke the promise.	false	Polysemy	glass	promise	M1_a_M2_b	V	2.357143	3	0.642857	14	1.008208	0.269455	M1_a	M2_b	-1	1.054093	broke
break	He broke the dish.	He broke the law.	false	Polysemy	dish	law	M1_b_M2_a	V	2.3	2.5	0.2	10	1.337494	0.422953	M1_b	M2_a	-0.307692	1.652504	broke
break	He broke the dish.	He broke the promise.	false	Polysemy	dish	promise	M1_b_M2_b	V	2.3	3	0.7	10	1.337494	0.422953	M1_b	M2_b	-0.222222	1.308594	broke
break	He broke the glass.	He broke the dish.	true	Polysemy	glass	dish	M1_a_M1_b	V	4	4	0	15	0	0	M1_a	M1_b	null	null	broke
break	He broke the law.	He broke the promise.	true	Polysemy	law	promise	M2_a_M2_b	V	3.692308	4	0.307692	13	0.480384	0.133235	M2_a	M2_b	null	null	broke
breakfast	They ate a pancake breakfast.	They ate a family breakfast.	false	Polysemy	pancake	family	M1_a_M2_a	N	3.785714	4	0.214286	14	0.578934	0.154727	M1_a	M2_a	0.421053	1.538968	breakfast
breakfast	They ate a pancake breakfast.	They ate a lonely breakfast.	false	Polysemy	pancake	lonely	M1_a_M2_b	N	3.941176	4	0.058824	17	0.242536	0.058824	M1_a	M2_b	-0.26087	1.483773	breakfast
breakfast	They ate a nutritious breakfast.	They ate a family breakfast.	false	Polysemy	nutritious	family	M1_b_M2_a	N	3.666667	4	0.333333	12	1.154701	0.333333	M1_b	M2_a	0.285714	1.683795	breakfast
breakfast	They ate a nutritious breakfast.	They ate a lonely breakfast.	false	Polysemy	nutritious	lonely	M1_b_M2_b	N	3.333333	4	0.666667	9	1.118034	0.372678	M1_b	M2_b	-1.045455	1.214095	breakfast
breakfast	They ate a pancake breakfast.	They ate a nutritious breakfast.	true	Polysemy	pancake	nutritious	M1_a_M1_b	N	4	4	0	5	0	0	M1_a	M1_b	null	null	breakfast
breakfast	They ate a family breakfast.	They ate a lonely breakfast.	true	Polysemy	family	lonely	M2_a_M2_b	N	3.8	4	0.2	20	0.615587	0.137649	M2_a	M2_b	null	null	breakfast
call	They called the landlord.	They called the loan.	false	Polysemy	landlord	loan	M1_a_M2_a	V	1.222222	1	0.222222	9	0.833333	0.277778	M1_a	M2_a	-1.529412	0.514496	called
call	They called the landlord.	They called the debt.	false	Polysemy	landlord	debt	M1_a_M2_b	V	1.3	1	0.3	10	1.418136	0.448454	M1_a	M2_b	-1.307692	1.1094	called
call	They called the police.	They called the loan.	false	Polysemy	police	loan	M1_b_M2_a	V	1.357143	1.5	0.142857	14	1.081818	0.289128	M1_b	M2_a	-1.666667	0.479463	called
call	They called the police.	They called the debt.	false	Polysemy	police	debt	M1_b_M2_b	V	1	1	0	16	1.032796	0.258199	M1_b	M2_b	-1.777778	0.427793	called
call	They called the landlord.	They called the police.	true	Polysemy	landlord	police	M1_a_M1_b	V	3.866667	4	0.133333	15	0.351866	0.090851	M1_a	M1_b	null	null	called
call	They called the loan.	They called the debt.	true	Polysemy	loan	debt	M2_a_M2_b	V	3.076923	3	0.076923	13	1.037749	0.28782	M2_a	M2_b	null	null	called
cape	It was a red cape.	It was a rocky cape.	false	Homonymy	red	rocky	M1_a_M2_a	N	0.090909	0	0.090909	11	0.301511	0.090909	M1_a	M2_a	-1.6	0.707107	cape
cape	It was a red cape.	It was an island cape.	false	Homonymy	red	island	M1_a_M2_b	N	0.181818	0	0.181818	11	0.40452	0.121967	M1_a	M2_b	-1.111111	1.367217	cape
cape	It was a flowing cape.	It was a rocky cape.	false	Homonymy	flowing	rocky	M1_b_M2_a	N	1	1	0	11	1.264911	0.381385	M1_b	M2_a	-1.428571	0.74642	cape
cape	It was a flowing cape.	It was an island cape.	false	Homonymy	flowing	island	M1_b_M2_b	N	0.428571	0	0.428571	14	0.937614	0.250588	M1_b	M2_b	-1.571429	0.513553	cape

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RAW-C: Relatedness of Ambiguous Words in Context

RAW-C contains relatedness judgments about ambiguous words in minimal pair contexts, e.g.,
"She liked the marinated lamb" vs. "She liked the friendly lamb".
The dataset was originally published in ACL 2021.

672 sentence pairs total
112 words (2 senses each, 2 sentences per sense)
Annotated for Same vs. Different Sense, and for Polysemy vs. Homonymy
Crowd-sourced relatedness judgments

Dataset Format

Main file: raw-c.csv
Key columns:

word: Target word
sentence1, sentence2: The sentence pair being contrasted
same: Whether the target word has the same or different meaning across sentences
ambiguity_type: Polysemy or Homonymy
mean_relatedness: Mean relatedness score across human participants
string: The actual surface form of the word in context
count: Number of annotators
sd_relatedness: Standard deviation of human ratings
dominance_sentence2: Mean dominance of sentence 2 relative to sentence 1
sd_dominance_sentence2: Standard deviation of dominance ratings

Dominance ratings are included only for Different Sense pairs.

Citation

If you use RAW-C in your research, please cite:

APA:
Trott, S., & Bergen, B. (2021). RAW-C: Relatedness of Ambiguous Words in Context (A New Lexical Resource for English).
Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (ACL-IJCNLP 2021).

BibTeX:

@inproceedings{trott-bergen-2021-raw,
  title = "{RAW}-{C}: Relatedness of Ambiguous Words in Context (A New Lexical Resource for {E}nglish)",
  author = "Trott, Sean  and Bergen, Benjamin",
  booktitle = "Proceedings of the 59th Annual Meeting of the Association for Computational Linguistics and the 11th International Joint Conference on Natural Language Processing (Volume 1: Long Papers)",
  year = "2021",
  publisher = "Association for Computational Linguistics",
  url = "https://aclanthology.org/2021.acl-long.550/",
  doi = "10.18653/v1/2021.acl-long.550",
  pages = "7077--7087"
}

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Size of downloaded dataset files:

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49 kB

Number of rows:

672