qinchuanhui/UDA-QA huggingface dataset:
Question: which multilingual approaches do they compare with?

Evidence:
raw_evidence: Table TABREF19 and TABREF26 report zero-shot results on Europarl and Multi-UN evaluation sets, respectively. We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. The results show that our approaches consistently outperform other approaches across languages and datasets, especially surpass pivoting, which is a strong baseline in the zero-shot scenario that multilingual NMT systems often fail to beat BIBREF19, BIBREF20, BIBREF23. Pivoting translates source to pivot then to target in two steps, causing inefficient translation process. Our approaches use one encoder-decoder model to translate between any zero-shot directions, which is more efficient than pivoting. Regarding the comparison between transfer approaches, our cross-lingual pretraining based transfer outperforms transfer method that does not use pretraining by a large margin.
raw_evidence: Table TABREF19 and TABREF26 report zero-shot results on Europarl and Multi-UN evaluation sets, respectively. We compare our approaches with related approaches of pivoting, multilingual NMT (MNMT) BIBREF19, and cross-lingual transfer without pretraining BIBREF16. The results show that our approaches consistently outperform other approaches across languages and datasets, especially surpass pivoting, which is a strong baseline in the zero-shot scenario that multilingual NMT systems often fail to beat BIBREF19, BIBREF20, BIBREF23. Pivoting translates source to pivot then to target in two steps, causing inefficient translation process. Our approaches use one encoder-decoder model to translate between any zero-shot directions, which is more efficient than pivoting. Regarding the comparison between transfer approaches, our cross-lingual pretraining based transfer outperforms transfer method that does not use pretraining by a large margin.

Answers:
BIBREF19, BIBREF20
multilingual NMT (MNMT) BIBREF19
Question: which datasets did they experiment with?

Evidence:
raw_evidence: We evaluate our cross-lingual pre-training based transfer approach against several strong baselines on two public datatsets, Europarl BIBREF31 and MultiUN BIBREF32, which contain multi-parallel evaluation data to assess the zero-shot performance. In all experiments, we use BLEU as the automatic metric for translation evaluation.
raw_evidence: We evaluate our cross-lingual pre-training based transfer approach against several strong baselines on two public datatsets, Europarl BIBREF31 and MultiUN BIBREF32, which contain multi-parallel evaluation data to assess the zero-shot performance. In all experiments, we use BLEU as the automatic metric for translation evaluation.

Answers:
Europarl, MultiUN
Europarl BIBREF31, MultiUN BIBREF32
Question: what is the source of the news sentences?

Evidence:
raw_evidence: In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. This dataset is comparable in size with the test sets of other languages (Table TABREF10 ). Included sentences are from political, sports, local and world news (Figures FIGREF8 , FIGREF9 ), covering the period between August 2012 and July 2018. The dataset provides annotations for 3 popular named entity classes: people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme. Tokens and sentences were segmented according to the UD standards for the Armenian language BIBREF11 .
raw_evidence: We used Sysoev and Andrianov's modification of the Nothman et al. approach to automatically generate data for training a named entity recognizer. This approach uses links between Wikipedia articles to generate sequences of named-entity annotated tokens.

Answers:
ilur.am
links between Wikipedia articles to generate sequences of named-entity annotated tokens
Question: did they use a crowdsourcing platform for manual annotations?

Evidence:
raw_evidence: In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. This dataset is comparable in size with the test sets of other languages (Table TABREF10 ). Included sentences are from political, sports, local and world news (Figures FIGREF8 , FIGREF9 ), covering the period between August 2012 and July 2018. The dataset provides annotations for 3 popular named entity classes: people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme. Tokens and sentences were segmented according to the UD standards for the Armenian language BIBREF11 .
During annotation, we generally relied on categories and guidelines assembled by BBN Technologies for TREC 2002 question answering track. Only named entities corresponding to BBN's person name category were tagged as PER. Those include proper names of people, including fictional people, first and last names, family names, unique nicknames. Similarly, organization name categories, including company names, government agencies, educational and academic institutions, sports clubs, musical ensembles and other groups, hospitals, museums, newspaper names, were marked as ORG. However, unlike BBN, we did not mark adjectival forms of organization names as named entities. BBN's gpe name, facility name, location name categories were combined and annotated as LOC.
raw_evidence: Instead of manually classifying Wikipedia articles as it was done in Nothman et al., we developed a rule-based classifier that used an article's Wikidata instance of and subclass of attributes to find the corresponding named entity type.
In order to evaluate the models trained on generated data, we manually annotated a named entities dataset comprising 53453 tokens and 2566 sentences selected from over 250 news texts from ilur.am. This dataset is comparable in size with the test sets of other languages (Table TABREF10 ). Included sentences are from political, sports, local and world news (Figures FIGREF8 , FIGREF9 ), covering the period between August 2012 and July 2018. The dataset provides annotations for 3 popular named entity classes: people (PER), organizations (ORG), and locations (LOC), and is released in CoNLL03 format with IOB tagging scheme. Tokens and sentences were segmented according to the UD standards for the Armenian language BIBREF11 .

Answers:
No
No
Question: what are the topics pulled from Reddit?

Evidence:
raw_evidence: Data collection. Subreddits are sub-communities on Reddit oriented around specific interests or topics, such as technology or politics. Sampling from Reddit as a whole would bias the model towards the most commonly discussed content. But by sampling posts from individual subreddits, we can control the kinds of posts we use to train our model. To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. All posts in our sample appeared between January 2007 and March 2015, and to control for length effects, contain between 300 and 400 characters. This results in a total training dataset of 5000 posts.
raw_evidence: Data collection. Subreddits are sub-communities on Reddit oriented around specific interests or topics, such as technology or politics. Sampling from Reddit as a whole would bias the model towards the most commonly discussed content. But by sampling posts from individual subreddits, we can control the kinds of posts we use to train our model. To collect a diverse training dataset, we have randomly sampled 1000 posts each from the subreddits politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. All posts in our sample appeared between January 2007 and March 2015, and to control for length effects, contain between 300 and 400 characters. This results in a total training dataset of 5000 posts.
We now apply our dogmatism classifier to a larger dataset of posts, examining how dogmatic language shapes the Reddit community. Concretely, we apply the BOW+LING model trained on the full Reddit dataset to millions of new unannotated posts, labeling these posts with a probability of dogmatism according to the classifier (0=non-dogmatic, 1=dogmatic). We then use these dogmatism annotations to address four research questions.

Answers:
politics, business, science, and AskReddit, and 1000 additional posts from the Reddit frontpage. 
training data has posts from politics, business, science and other popular topics; the trained model is applied to millions of unannotated posts on all of Reddit
Question: What predictive model do they build?

Evidence:
raw_evidence: We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. BOW and SENT provide baselines for the task. We compute BOW features using term frequency-inverse document frequency (TF-IDF) and category-based features by normalizing counts for each category by the number of words in each document. The BOW classifiers are trained with regularization (L2 penalties of 1.5).
raw_evidence: We compare the predictions of logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features. BOW and SENT provide baselines for the task. We compute BOW features using term frequency-inverse document frequency (TF-IDF) and category-based features by normalizing counts for each category by the number of words in each document. The BOW classifiers are trained with regularization (L2 penalties of 1.5).

Answers:
logistic regression models
logistic regression models based on unigram bag-of-words features (BOW), sentiment signals (SENT), the linguistic features from our earlier analyses (LING), and combinations of these features.
Question: What crowdsourcing platform is used?

Evidence:
raw_evidence: With the purpose of obtaining evaluation datasets from crowd annotators, we collect the sentences from two domains: Dialog and E-commerce domain. We hire undergraduate students to annotate the sentences. They are required to identify the predefined types of entities in the sentences. Together with the guideline document, the annotators are educated some tips in fifteen minutes and also provided with 20 exemplifying sentences.

Answers:
They did not use any platform, instead they hired undergraduate students to do the annotation.
Question: How do they match words before reordering them?

Evidence:
raw_evidence: We use the CFILT-preorder system for reordering English sentences to match the Indian language word order. It contains two re-ordering systems: (1) generic rules that apply to all Indian languages BIBREF17 , and (2) hindi-tuned rules which improve the generic rules by incorporating improvements found through an error analysis of English-Hindi reordering BIBREF28 . These Hindi-tuned rules have been found to improve reordering for many English to Indian language pairs BIBREF29 .

Answers:
CFILT-preorder system
Question: On how many language pairs do they show that preordering assisting language sentences helps translation quality?

Evidence:
raw_evidence: We experimented with English INLINEFORM0 Hindi translation as the parent task. English is the assisting source language. Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks. Hindi, Bengali, Gujarati and Marathi are Indo-Aryan languages, while Malayalam and Tamil are Dravidian languages. All these languages have a canonical SOV word order.
raw_evidence: Languages
We experimented with English INLINEFORM0 Hindi translation as the parent task. English is the assisting source language. Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks. Hindi, Bengali, Gujarati and Marathi are Indo-Aryan languages, while Malayalam and Tamil are Dravidian languages. All these languages have a canonical SOV word order.

Answers:
5
Bengali, Gujarati, Marathi, Malayalam and Tamil are the primary source languages, and translation from these to Hindi constitute the child tasks.
Question: Which dataset(s) do they experiment with?

Evidence:
raw_evidence: Datasets
For training English-Hindi NMT systems, we use the IITB English-Hindi parallel corpus BIBREF22 ( INLINEFORM0 sentences from the training set) and the ILCI English-Hindi parallel corpus ( INLINEFORM1 sentences). The ILCI (Indian Language Corpora Initiative) multilingual parallel corpus BIBREF23 spans multiple Indian languages from the health and tourism domains. We use the 520-sentence dev-set of the IITB parallel corpus for validation. For each child task, we use INLINEFORM2 sentences from ILCI corpus as the test set.
raw_evidence: For training English-Hindi NMT systems, we use the IITB English-Hindi parallel corpus BIBREF22 ( INLINEFORM0 sentences from the training set) and the ILCI English-Hindi parallel corpus ( INLINEFORM1 sentences). The ILCI (Indian Language Corpora Initiative) multilingual parallel corpus BIBREF23 spans multiple Indian languages from the health and tourism domains. We use the 520-sentence dev-set of the IITB parallel corpus for validation. For each child task, we use INLINEFORM2 sentences from ILCI corpus as the test set.

Answers:
IITB English-Hindi parallel corpus BIBREF22, ILCI English-Hindi parallel corpus
IITB English-Hindi parallel corpus, ILCI English-Hindi parallel corpus
Question: Which information about text structure is included in the corpus?

Evidence:
raw_evidence: The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).
Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer
raw_evidence: The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).
Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation was added as part of a textspan element in the textstructure layer

Answers:
paragraphs, lines, Information on physical page segmentation (for PDFs only), paragraph segmentation, and line segmentation
paragraph, lines, textspan element (paragraph segmentation, line segmentation, Information on physical page segmentation(for PDF only))
Question: Which information about typography is included in the corpus?

Evidence:
raw_evidence: The paper at hand introduces a corpus developed for use in automatic readability assessment and automatic text simplification of German. The focus of this publication is on representing information that is valuable for these tasks but that hitherto has largely been ignored in machine learning approaches centering around simplified language, specifically, text structure (e.g., paragraphs, lines), typography (e.g., font type, font style), and image (content, position, and dimensions) information. The importance of considering such information has repeatedly been asserted theoretically BIBREF11, BIBREF12, BIBREF0. The remainder of this paper is structured as follows: Section SECREF2 presents previous corpora used for automatic readability assessment and text simplification. Section SECREF3 describes our corpus, introducing its novel aspects and presenting the primary data (Section SECREF7), the metadata (Section SECREF10), the secondary data (Section SECREF28), the profile (Section SECREF35), and the results of machine learning experiments carried out on the corpus (Section SECREF37).
Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer (cf. Figure FIGREF34 for an example)
raw_evidence: Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer (cf. Figure FIGREF34 for an example)
A separate fonts layer was introduced to preserve detailed information on the font configurations referenced in the tokens layer
For the webpages, a static dump of all documents was created. Following this, the documents were manually checked to verify the language. The main content was subsequently extracted, i.e., HTML markup and boilerplate removed using the Beautiful Soup library for Python. Information on text structure (e.g., paragraphs, lines) and typography (e.g., boldface, italics) was retained. Similarly, image information (content, position, and dimensions of an image) was preserved.

Answers:
font type, font style, Information on the font type and font style (e.g., italics, bold print) of a token and its position on the physical page
font type and font style (e.g., italics, bold print) of a token and its position on the physical page (for PDFs only) was specified as attributes to the token elements of the tokens layer, A separate fonts layer was introduced to preserve detailed information on the font configurations referenced in the tokens layer
Question: What does KBQA abbreviate for

Evidence:
raw_evidence: Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the process used to parse two sample questions in a KBQA system: (a) a single-relation question, which can be answered with a single $<$ head-entity, relation, tail-entity $>$ KB tuple BIBREF6 , BIBREF7 , BIBREF2 ; and (b) a more complex case, where some constraints need to be handled for multiple entities in the question. The KBQA system in the figure performs two key tasks: (1) entity linking, which links $n$ -grams in questions to KB entities, and (2) relation detection, which identifies the KB relation(s) a question refers to.
raw_evidence: Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the process used to parse two sample questions in a KBQA system: (a) a single-relation question, which can be answered with a single $<$ head-entity, relation, tail-entity $>$ KB tuple BIBREF6 , BIBREF7 , BIBREF2 ; and (b) a more complex case, where some constraints need to be handled for multiple entities in the question. The KBQA system in the figure performs two key tasks: (1) entity linking, which links $n$ -grams in questions to KB entities, and (2) relation detection, which identifies the KB relation(s) a question refers to.

Answers:
Knowledge Base Question Answering
Knowledge Base Question Answering
Question: What is te core component for KBQA?

Evidence:
raw_evidence: Knowledge Base Question Answering (KBQA) systems answer questions by obtaining information from KB tuples BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 . For an input question, these systems typically generate a KB query, which can be executed to retrieve the answers from a KB. Figure 1 illustrates the process used to parse two sample questions in a KBQA system: (a) a single-relation question, which can be answered with a single $<$ head-entity, relation, tail-entity $>$ KB tuple BIBREF6 , BIBREF7 , BIBREF2 ; and (b) a more complex case, where some constraints need to be handled for multiple entities in the question. The KBQA system in the figure performs two key tasks: (1) entity linking, which links $n$ -grams in questions to KB entities, and (2) relation detection, which identifies the KB relation(s) a question refers to.
raw_evidence: Our main contributions include: (i) An improved relation detection model by hierarchical matching between questions and relations with residual learning; (ii) We demonstrate that the improved relation detector enables our simple KBQA system to achieve state-of-the-art results on both single-relation and multi-relation KBQA tasks.

Answers:
answer questions by obtaining information from KB tuples 
hierarchical matching between questions and relations with residual learning