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id stringlengths 40 40	pid stringlengths 42 42	input stringlengths 8.37k 169k	output stringlengths 1 1.63k
dad8cc543a87534751f9f9e308787e1af06f0627	dad8cc543a87534751f9f9e308787e1af06f0627_1	Q: What datasets used for evaluation? Text: Introduction Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usua...	AIDA-CoNLL, ACE2004, MSNBC, AQUAINT, WNED-CWEB, WNED-WIKI, OURSELF-WIKI
0481a8edf795768d062c156875d20b8fb656432c	0481a8edf795768d062c156875d20b8fb656432c_0	Q: what are the mentioned cues? Text: Introduction Entity Linking (EL), which is also called Entity Disambiguation (ED), is the task of mapping mentions in text to corresponding entities in a given knowledge Base (KB). This task is an important and challenging stage in text understanding because mentions are usually am...	output of global LSTM network at time $V_{m_i}^t$5 , which encodes the mention context and target entity information from $V_{m_i}^t$6 to $V_{m_i}^t$7
b6a4ab009e6f213f011320155a7ce96e713c11cf	b6a4ab009e6f213f011320155a7ce96e713c11cf_0	Q: How did the author's work rank among other submissions on the challenge? Text: Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other ...	Unanswerable
cfffc94518d64cb3c8789395707e4336676e0345	cfffc94518d64cb3c8789395707e4336676e0345_0	Q: What approaches without reinforcement learning have been tried? Text: Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question ...	classification, regression, neural methods
cfffc94518d64cb3c8789395707e4336676e0345	cfffc94518d64cb3c8789395707e4336676e0345_1	Q: What approaches without reinforcement learning have been tried? Text: Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question ...	Support Vector Regression (SVR) and Support Vector Classification (SVC), deep learning regression models of BIBREF2 to convert them to classification models
f60629c01f99de3f68365833ee115b95a3388699	f60629c01f99de3f68365833ee115b95a3388699_0	Q: What classification approaches were experimented for this task? Text: Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other question ...	NNC SU4 F1, NNC top 5, Support Vector Classification (SVC)
a7cb4f8e29fd2f3d1787df64cd981a6318b65896	a7cb4f8e29fd2f3d1787df64cd981a6318b65896_0	Q: Did classification models perform better than previous regression one? Text: Introduction The BioASQ Challenge includes a question answering task (Phase B, part B) where the aim is to find the “ideal answer” — that is, an answer that would normally be given by a person BIBREF0. This is in contrast with most other qu...	Yes
642c4704a71fd01b922a0ef003f234dcc7b223cd	642c4704a71fd01b922a0ef003f234dcc7b223cd_0	Q: What are the main sources of recall errors in the mapping? Text: Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its ow...	irremediable annotation discrepancies, differences in choice of attributes to annotate, The resources themselves would need updating to encode the relevant morphosyntactic information. Some languages had a very low number of overlapping forms, and no tag matches or near-matches between them, the two annotations encode ...
e477e494fe15a978ff9c0a5f1c88712cdaec0c5c	e477e494fe15a978ff9c0a5f1c88712cdaec0c5c_0	Q: Do they look for inconsistencies between different languages' annotations in UniMorph? Text: Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are a...	Yes
04495845251b387335bf2e77e2c423130f43c7d9	04495845251b387335bf2e77e2c423130f43c7d9_0	Q: Do they look for inconsistencies between different UD treebanks? Text: Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to ...	Yes
564dcaf8d0bcc274ab64c784e4c0f50d7a2c17ee	564dcaf8d0bcc274ab64c784e4c0f50d7a2c17ee_0	Q: Which languages do they validate on? Text: Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema...	Ar, Bg, Ca, Cs, Da, De, En, Es, Eu, Fa, Fi, Fr, Ga, He, Hi, Hu, It, La, Lt, Lv, Nb, Nl, Nn, PL, Pt, Ro, Ru, Sl, Sv, Tr, Uk, Ur
564dcaf8d0bcc274ab64c784e4c0f50d7a2c17ee	564dcaf8d0bcc274ab64c784e4c0f50d7a2c17ee_1	Q: Which languages do they validate on? Text: Introduction The two largest standardized, cross-lingual datasets for morphological annotation are provided by the Universal Dependencies BIBREF1 and Universal Morphology BIBREF2 , BIBREF3 projects. Each project's data are annotated according to its own cross-lingual schema...	We apply this conversion to the 31 languages, Arabic, Hindi, Lithuanian, Persian, and Russian. , Dutch, Spanish
f3d0e6452b8d24b7f9db1fd898d1fbe6cd23f166	f3d0e6452b8d24b7f9db1fd898d1fbe6cd23f166_0	Q: Does the paper evaluate any adjustment to improve the predicion accuracy of face and audio features? Text: Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted...	No
9b1d789398f1f1a603e4741a5eee63ccaf0d4a4f	9b1d789398f1f1a603e4741a5eee63ccaf0d4a4f_0	Q: How is face and audio data analysis evaluated? Text: Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utteran...	confusion matrices, $\text{F}_1$ score
00bcdffff7e055f99aaf1b05cf41c98e2748e948	00bcdffff7e055f99aaf1b05cf41c98e2748e948_0	Q: What is the baseline method for the task? Text: Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., a transcribed utterance, a...	For the emotion recognition from text they use described neural network as baseline. For audio and face there is no baseline.
f92ee3c5fce819db540bded3cfcc191e21799cb1	f92ee3c5fce819db540bded3cfcc191e21799cb1_0	Q: What are the emotion detection tools used for audio and face input? Text: Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., ...	We apply an off-the-shelf tool for emotion recognition (the manufacturer cannot be disclosed due to licensing restrictions)
f92ee3c5fce819db540bded3cfcc191e21799cb1	f92ee3c5fce819db540bded3cfcc191e21799cb1_1	Q: What are the emotion detection tools used for audio and face input? Text: Introduction Automatic emotion recognition is commonly understood as the task of assigning an emotion to a predefined instance, for example an utterance (as audio signal), an image (for instance with a depicted face), or a textual unit (e.g., ...	cannot be disclosed due to licensing restrictions
4547818a3bbb727c4bb4a76554b5a5a7b5c5fedb	4547818a3bbb727c4bb4a76554b5a5a7b5c5fedb_0	Q: what amounts of size were used on german-english? Text: Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and ...	Training data with 159000, 80000, 40000, 20000, 10000 and 5000 sentences, and 7584 sentences for development
4547818a3bbb727c4bb4a76554b5a5a7b5c5fedb	4547818a3bbb727c4bb4a76554b5a5a7b5c5fedb_1	Q: what amounts of size were used on german-english? Text: Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and ...	ultra-low data condition (100k words of training data) and the full IWSLT 14 training corpus (3.2M words)
07d7652ad4a0ec92e6b44847a17c378b0d9f57f5	07d7652ad4a0ec92e6b44847a17c378b0d9f57f5_0	Q: what were their experimental results in the low-resource dataset? Text: Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-i...	10.37 BLEU
9f3444c9fb2e144465d63abf58520cddd4165a01	9f3444c9fb2e144465d63abf58520cddd4165a01_0	Q: what are the methods they compare with in the korean-english dataset? Text: Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly da...	gu-EtAl:2018:EMNLP1
2348d68e065443f701d8052018c18daa4ecc120e	2348d68e065443f701d8052018c18daa4ecc120e_0	Q: what pitfalls are mentioned in the paper? Text: Introduction While neural machine translation (NMT) has achieved impressive performance in high-resource data conditions, becoming dominant in the field BIBREF0 , BIBREF1 , BIBREF2 , recent research has argued that these models are highly data-inefficient, and underper...	highly data-inefficient, underperform phrase-based statistical machine translation
5679fabeadf680e35a4f7b092d39e8638dca6b4d	5679fabeadf680e35a4f7b092d39e8638dca6b4d_0	Q: Does the paper report the results of previous models applied to the same tasks? Text: Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and informat...	Yes
5679fabeadf680e35a4f7b092d39e8638dca6b4d	5679fabeadf680e35a4f7b092d39e8638dca6b4d_1	Q: Does the paper report the results of previous models applied to the same tasks? Text: Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and informat...	No
a939a53cabb4893b2fd82996f3dbe8688fdb7bbb	a939a53cabb4893b2fd82996f3dbe8688fdb7bbb_0	Q: How is the quality of the discussion evaluated? Text: Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-...	Unanswerable
8b99767620fd4efe51428b68841cc3ec06699280	8b99767620fd4efe51428b68841cc3ec06699280_0	Q: What is the technique used for text analysis and mining? Text: Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger...	Unanswerable
312417675b3dc431eb7e7b16a917b7fed98d4376	312417675b3dc431eb7e7b16a917b7fed98d4376_0	Q: What are the causal mapping methods employed? Text: Introduction ::: Background Over the past two decades, the rise of social media and the digitization of news and discussion platforms have radically transformed how individuals and groups create, process and share news and information. As Alan Rusbridger, former-ed...	Axelrod's causal mapping method
792d7b579cbf7bfad8fe125b0d66c2059a174cf9	792d7b579cbf7bfad8fe125b0d66c2059a174cf9_0	Q: What is the previous work's model? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively...	Ternary Trans-CNN
44a2a8e187f8adbd7d63a51cd2f9d2d324d0c98d	44a2a8e187f8adbd7d63a51cd2f9d2d324d0c98d_0	Q: What dataset is used? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, bu...	HEOT , A labelled dataset for a corresponding english tweets
44a2a8e187f8adbd7d63a51cd2f9d2d324d0c98d	44a2a8e187f8adbd7d63a51cd2f9d2d324d0c98d_1	Q: What dataset is used? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, bu...	HEOT
5908d7fb6c48f975c5dfc5b19bb0765581df2b25	5908d7fb6c48f975c5dfc5b19bb0765581df2b25_0	Q: How big is the dataset? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, ...	3189 rows of text messages
5908d7fb6c48f975c5dfc5b19bb0765581df2b25	5908d7fb6c48f975c5dfc5b19bb0765581df2b25_1	Q: How big is the dataset? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, ...	Resulting dataset was 7934 messages for train and 700 messages for test.
cca3301f20db16f82b5d65a102436bebc88a2026	cca3301f20db16f82b5d65a102436bebc88a2026_0	Q: How is the dataset collected? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to H...	A labelled dataset for a corresponding english tweets were also obtained from a study conducted by Davidson et al, HEOT obtained from one of the past studies done by Mathur et al
cfd67b9eeb10e5ad028097d192475d21d0b6845b	cfd67b9eeb10e5ad028097d192475d21d0b6845b_0	Q: Was each text augmentation technique experimented individually? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, ...	No
e1c681280b5667671c7f78b1579d0069cba72b0e	e1c681280b5667671c7f78b1579d0069cba72b0e_0	Q: What models do previous work use? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively ...	Ternary Trans-CNN , Hybrid multi-channel CNN and LSTM
58d50567df71fa6c3792a0964160af390556757d	58d50567df71fa6c3792a0964160af390556757d_0	Q: Does the dataset contain content from various social media platforms? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi lang...	No
07c79edd4c29635dbc1c2c32b8df68193b7701c6	07c79edd4c29635dbc1c2c32b8df68193b7701c6_0	Q: What dataset is used? Text: Introduction Hinglish is a linguistic blend of Hindi (very widely spoken language in India) and English (an associate language of urban areas) and is spoken by upwards of 350 million people in India. While the name is based on the Hindi language, it does not refer exclusively to Hindi, bu...	HEOT , A labelled dataset for a corresponding english tweets
66125cfdf11d3bf8e59728428e02021177142c3a	66125cfdf11d3bf8e59728428e02021177142c3a_0	Q: How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment? Text: Introduction Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBR...	Table TABREF15 shows that word-alignment based on mBERT representations surpasses the outputs of the standard FastAlign tool even if it was provided large parallel corpus. This suggests that word-level semantics are well captured by mBERT contextual embeddings. For this task, learning an explicit projection had a negli...
66125cfdf11d3bf8e59728428e02021177142c3a	66125cfdf11d3bf8e59728428e02021177142c3a_1	Q: How they demonstrate that language-neutral component is sufficiently general in terms of modeling semantics to allow high-accuracy word-alignment? Text: Introduction Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBR...	explicit projection had a negligible effect on the performance
222b2469eede9a0448e0226c6c742e8c91522af3	222b2469eede9a0448e0226c6c742e8c91522af3_0	Q: Are language-specific and language-neutral components disjunctive? Text: Introduction Multilingual BERT (mBERT; BIBREF0) is gaining popularity as a contextual representation for various multilingual tasks, such as dependency parsing BIBREF1, BIBREF2, cross-lingual natural language inference (XNLI) or named-entity re...	No