DataHammer/scimrc huggingface dataset:
Question: Based on this paragraph, what is the diameter of a 10 F Foley catheter in mm?
Evidence: The relative size of a Foley catheter is described using French units (F). Alternatively, the size of a 10 F catheter might be expressed as 10 Ch (Charriere units – named after a 19th century French scientific instrument maker, Joseph-Frédéric-Benoît Charrière). The most common sizes are 10 F to 28 F. 1 F is equivalent to 0.33 mm = .013" = 1/77" of diameter. Foley catheters are usually color coded by size with a solid color band at the external end of the balloon inflation tube, allowing for easy identification of the size. Note: Colors for French sizes 5, 6, 8, 10 may vary significantly if intended for pediatric patients. Color for French size 26 may also be pink instead of black.
https://en.wikipedia.org/wiki/Foley_catheter
Answer: According to this paragraph, the diameter of a 1 F catheter is 1/77mm so the diameter of a 10 F catheter is 10/77mm.
Question: Do they fine-tune the used word embeddings on their medical texts?
Evidence: Three most common classic non-contextual approaches to obtain word embeddings are skip-gram, Continuous Bag of Words (two algorithms from BIBREF19 ) and GloVe (Global Vectors, BIBREF20 , where higher accuracy than in previous algorithms was proved). Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts.
Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately. We compute two separate embeddings, because we want to catch the similarity between terms in their specific context, i.e. words similar in the interview may not be similar in the examination description (for example we computed that the nearest words to cough in interview descriptions was runny nose, sore throat, fever, dry cough but in examination description it was rash, sunny, laryngeal, dry cough).
Some authors use pretrained embeddings (especially when their data set is too small to train their own embeddings) or try to modify these embeddings and adjust to their set. But the biggest drawback of these approaches is that the corpus for training embeddings can be not related to the specific task where embeddings are utilized. A lot of medical concepts are not contained in well-known embeddings bases. Furthermore, the similarity of words may vary in different contexts.
Then, we compute embeddings of concepts (by GloVe) for interview descriptions and for examination descriptions separately.
Answer: No.
Question: Classify each of the following as old or young: baby, child, grandma, elderly, grandpa, octogenarian, infant
Answer: Baby: Young
Child: Young
Grandma: Old
Elderly: Old
Grandpa: Old
Octogenarian: Old
Infant: Young
Question: In the work of Danescu-Niculescu-Mizil et al., how do they adopt to measure the distinctiveness of memorable movie quotes?
Evidence: Training Language Models (LMs) is a straightforward way to collect a set of rules by utilizing the fact that words do not appear in an arbitrary order; we in fact can gain useful information about a word by knowing the company it keeps BIBREF7 . A statistical language model estimates the probability of a sequence of words or an upcoming word. An N-gram is a contiguous sequence of N words: a unigram is a single word, a bigram is a two-word sequence, and a trigram is a three-word sequence. For example, in the tweet
tears in Ramen #SingleLifeIn3Words
“tears”, “in”, “Ramen” and “#SingleLifeIn3Words” are unigrams; “tears in”, “in Ramen” and “Ramen #SingleLifeIn3Words” are bigrams and “tears in Ramen” and “in Ramen #SingleLifeIn3Words” are trigrams.
An N-gram model can predict the next word from a sequence of N-1 previous words. A trigram Language Model (LM) predicts the conditional probability of the next word using the following approximation: DISPLAYFORM0 
The assumption that the probability of a word depends only on a small number of previous words is called a Markov assumption BIBREF8 . Given this assumption the probability of a sentence can be estimated as follows: DISPLAYFORM0 
In a study on how phrasing affects memorability, BIBREF9 take a language model approach to measure the distinctiveness of memorable movie quotes. They do this by evaluating a quote with respect to a “common language” model built from the newswire sections of the Brown corpus BIBREF10 . They find that movie quotes which are less like “common language” are more distinctive and therefore more memorable. The intuition behind our approach is that humor should in some way be memorable or distinct, and so tweets that diverge from a “common language” model would be expected to be funnier.
In order to evaluate how funny a tweet is, we train language models on two datasets: the tweet data and the news data. Tweets that are more probable according to the tweet data language model are ranked as being funnier. However, tweets that have a lower probability according to the news language model are considered the funnier since they are the least like the (unfunny) news corpus. We relied on both bigrams and trigrams when training our models.
We use KenLM BIBREF11 as our language modeling tool. Language models are estimated using modified Kneser-Ney smoothing without pruning. KenLM also implements a back-off technique so if an N-gram is not found, KenLM applies the lower order N-gram's probability along with its back-off weights.
In a study on how phrasing affects memorability, (Danescu-Niculescu-Mizil et al., 2012) take a language model approach to measure the distinctiveness of memorable movie quotes.They do this by evaluating a quote with respect to a “common language” model built from the newswire sections of the Brown corpus (Kucera and Francis, 1967).
Answer: They take a language model approach, by evaluating a quote with respect to a “common language” model built from the newswire sections of the Brown corpus.
Question: What encoders are used in the task？
Evidence: Given an entity in most of the existing knowledge bases, there is always an available corresponding text description with valuable semantic information for this entity, which can provide beneficial supplement for entity representation.
To encode the representation of a entity from its text description, we need to encode the variable-length sentence to a fixed-length vector. There are several kinds of neural models used in sentence modeling. These models generally consist of a projection layer that maps words, sub-word units or n-grams to vector representations (often trained beforehand with unsupervised methods), and then combine them with the different architectures of neural networks, such as neural bag-of-words (NBOW), recurrent neural network (RNN) BIBREF9 , BIBREF10 , BIBREF11 and convolutional neural network (CNN) BIBREF12 , BIBREF13 .
In this paper, we use three encoders (NBOW, LSTM and attentive LSTM) to model the text descriptions.
In this paper, we use three encoders (NBOW, LSTM and attentive LSTM) to model the text descriptions.
Answer: They use three encoders (NBOW, LSTM and attentive LSTM) to model the text descriptions.
Question: Name the main crochet stitches
Answer: The main crochet stitches include the single crochet stitch, the double crochet stitch, the half double crochet stitch, the treble stitch and the slip stitch
Question: What is the Masters?
Answer: The Masters Tournament is a golf tournament held annually in the first week of April at Augusta National Golf Club in Augusta, Georgia.  The Masters is one of four Major golf tournaments and the only one to be played at the same course every year.  The course is renowned for its iconic holes, impeccable groundskeeping, and colorful flowers that are typically in bloom.  The winner earns a coveted Green Jacket and a lifetime invitation back to compete.  Many players and fans consider The Masters to be their favorite tournament given these traditions and the historical moments that have occurred in past tournaments.
Question: Tell me which continent the following cities are in:
- Seattle
- Helsinki 
- Lagos
- Osaka
- Istanbul
Answer: Seattle is located in North America
Helsinki is in Europe
Lagos is in Africa
Osaka is in Asia
Istanbul is partially located in both Europe and Asia
Question: I’m playing $1/$2 poker with friends. A friend had AK and I had AA on the button. I raised to $15 after a small raise before me and my friend with AK called. The other player folded. 

The flop came K, 8, 4 with no common suits. My friend checked and I bet 1/2 pot. He called. 

The next card was a 10 and I bet full pot (about $70) and he raised me all in to about $200. I called. 

The next card was meaningless and I won the pot. 

Did he play the hand well?
Answer: He played the hand aggressively, but not poorly. He had to assume he had the lead after the flop. His best move would have been to call your aggressive pot bet instead of go all in, but given he had top pair with top kicker, it’s not a bad move. He was unlucky to be going against aces.
Question: How many participants registered for the shared task?
Evidence: The past decade witnessed rapid growth and widespread usage of social media platforms by generating a significant amount of user-generated text. The user-generated texts contain high information content in the form of news, expression, or knowledge. Automatically mining information from user-generated data is unraveling a new field of research in Natural Language Processing (NLP) and has been a difficult task due to unstructured and noisy nature. In spite of the existing challenges, much research has been conducted on user-generated data in the field of information extraction, sentiment analysis, event extraction, user profiling and many more.
According to Census of India, there are 22 scheduled languages and more than 100 non scheduled languages in India. There are 462 million internet users in India and most people know more than one language. They express their feelings or emotions using more than one languages, thus generating a new code-mixed/code-switched language. The problem of code-mixing and code-switching are well studied in the field of NLP BIBREF0 , BIBREF1 . Information extraction from Indian internet user-generated texts become more difficult due to this multilingual nature. Much research has been conducted in this field such as language identification BIBREF2 , BIBREF3 , part-of-speech tagging BIBREF4 . Joshi et al. JoshiPSV16 have performed sentiment analysis in Hindi-English (HI-EN) code-mixed data and almost no work exists on sentiment analysis of Bengali-English (BN-EN) code-mixed texts. The Sentiment Analysis of Indian Language (Code-Mixed) (SAIL _Code-Mixed) is a shared task at ICON-2017. Two most popular code-mixed languages namely Hindi and Bengali mixed with English were considered for the sentiment identification task. A total of 40 participants registered for the shared task and only nine teams have submitted their predicted outputs. Out of nine unique submitted systems for evaluation, eight teams submitted fourteen runs for HI-EN dataset whereas seven teams submitted nine runs for BN-EN dataset. The training and test dataset were provided after annotating the languages and sentiment (positive, negative, and neutral) tags. The language tags were automatically annotated with the help of different dictionaries whereas the sentiment tags were manually annotated. The submitted systems are ranked using the macro average f-score.
The paper is organized as following manner. Section SECREF2 describes the NLP in Indian languages mainly related to code-mixing and sentiment analysis. The detailed statistics of the dataset and evaluation are described in Section SECREF3 . The baseline systems and participant's system description are described in Section SECREF4 . Finally, conclusion and future research are drawn in Section SECREF5 .
A total of 40 participants registered for the shared task and only nine teams have submitted their predicted outputs.
Answer: 40 participants.
Question: Has the ranking loss function introduced in this paper been investigated in the literature for relation classification before?
Evidence: Relation classification is the task of assigning sentences with two marked entities to a predefined set of relations. The sentence “We poured the <e1>milk</e1> into the <e2>pumpkin mixture</e2>.”, for example, expresses the relation Entity-Destination(e1,e2). While early research mostly focused on support vector machines or maximum entropy classifiers BIBREF0 , BIBREF1 , recent research showed performance improvements by applying neural networks (NNs) BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 on the benchmark data from SemEval 2010 shared task 8 BIBREF8 .
This study investigates two different types of NNs: recurrent neural networks (RNNs) and convolutional neural networks (CNNs) as well as their combination. We make the following contributions:
(1) We propose extended middle context, a new context representation for CNNs for relation classification. The extended middle context uses all parts of the sentence (the relation arguments, left of the relation arguments, between the arguments, right of the arguments) and pays special attention to the middle part.
(2) We present connectionist bi-directional RNN models which are especially suited for sentence classification tasks since they combine all intermediate hidden layers for their final decision. Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before.
(3) Finally, we combine CNNs and RNNs using a simple voting scheme and achieve new state-of-the-art results on the SemEval 2010 benchmark dataset.
Furthermore, the ranking loss function is introduced for the RNN model optimization which has not been investigated in the literature for relation classification before.
Answer: No.
Question: From the passage provided, extract the year Pro Kabaddi League was established.
Evidence: Asian Games
Main article: Kabaddi at the Asian Games
(video) Kabaddi being played in Japan, 2015
Kabaddi was played as a demonstration event at the First Asian Games in 1951, and again in 1982, before becoming a medal event for the first time in 1990.

The Indian national team won every men's and women's kabaddi competition in the Asian Games from 2002 through 2014. At the 2018 Asian Games, Iran became the first country other than India to win gold medals in kabaddi, with India's men's team winning bronze, and India's women's team being beaten by Iran to win silver.

Pro Kabaddi League
Main article: Pro Kabaddi League
The Pro Kabaddi League was established in 2014. The league modeled its business upon that of the Indian Premier League of Twenty20 cricket, with a large focus on marketing, the backing of local broadcaster Star Sports, and changes to the sport's rules and its presentation to make it more suitable for a television audience. The Pro Kabaddi League quickly became a ratings success on Indian television; the 2014 season was watched by at least 435 million viewers over the course of the season, and the inaugural championship match was seen by 98.6 million viewers.

Additional rules are used in the Pro Kabaddi League to encourage scoring: when a defensive side has three or fewer players remaining, tackles are worth two points instead of one. Furthermore, if a team performs two empty raids in a row, the next raider must score a point, or else they will be declared out and the opposing team will score a point.
Answer: The Pro Kabaddi League was established in 2014
Question: How are relations used to propagate polarity?
Evidence: In this paper, we propose a simple and effective method for learning affective events that only requires a very small seed lexicon and a large raw corpus. As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.
As illustrated in Figure FIGREF1, our key idea is that we can exploit discourse relations BIBREF4 to efficiently propagate polarity from seed predicates that directly report one's emotions (e.g., “to be glad” is positive). Suppose that events $x_1$ are $x_2$ are in the discourse relation of Cause (i.e., $x_1$ causes $x_2$). If the seed lexicon suggests $x_2$ is positive, $x_1$ is also likely to be positive because it triggers the positive emotion. The fact that $x_2$ is known to be negative indicates the negative polarity of $x_1$. Similarly, if $x_1$ and $x_2$ are in the discourse relation of Concession (i.e., $x_2$ in spite of $x_1$), the reverse of $x_2$'s polarity can be propagated to $x_1$. Even if $x_2$'s polarity is not known in advance, we can exploit the tendency of $x_1$ and $x_2$ to be of the same polarity (for Cause) or of the reverse polarity (for Concession) although the heuristic is not exempt from counterexamples. We transform this idea into objective functions and train neural network models that predict the polarity of a given event.
Answer: based on the relation between events, the suggested polarity of one event can determine the possible polarity of the other event
Question: What are the five romance languages?
Answer: The 5 romance languages in order of number of speakers are: Spanish, Portuguese, French, Italian and Romanian.
Question: What is new state-of-the-art performance on CoNLL-2009 dataset?
Evidence: Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings. Notice that our best Closed model can almost perform as well as the state-of-the-art model while the latter utilizes pre-trained word embeddings. Besides, performance gap between three models under Open setting is very small. It indicates that the representation ability of BERT is so powerful and may contains rich syntactic information. At last, the Gold result is much higher than the other models, indicating that there is still large space for improvement for this task.
FLOAT SELECTED: Table 7: SRL results on the Chinese test set. We choose the best settings for each configuration of our model.
Table TABREF46 shows that our Open model achieves more than 3 points of f1-score than the state-of-the-art result, and RelAwe with DepPath&RelPath achieves the best in both Closed and Open settings.
FLOAT SELECTED: Table 7: SRL results on the Chinese test set. We choose the best settings for each configuration of our model.
Answer: In closed setting 84.22 F1 and in open 87.35 F1.
Question: Why do they want unsupervised solutions to modern topic identification systems?
Evidence: Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable. In this paper, we investigate alternative unsupervised solutions to obtaining tokenizations of speech in terms of a vocabulary of automatically discovered word-like or phoneme-like units, without depending on the supervised training of ASR systems. Moreover, using automatic phoneme-like tokenizations, we demonstrate that a convolutional neural network based framework for learning spoken document representations provides competitive performance compared to a standard bag-of-words representation, as evidenced by comprehensive topic ID evaluations on both single-label and multi-label classification tasks.
Modern topic identification (topic ID) systems for speech use automatic speech recognition (ASR) to produce speech transcripts, and perform supervised classification on such ASR outputs. However, under resource-limited conditions, the manually transcribed speech required to develop standard ASR systems can be severely limited or unavailable.
Answer: Because if the resource is limited, the manually transcribed speech required by developing standard ASR systems may also be severely limited or unavailable.
Question: Does the paper motivate the use of CRF as the baseline model?
Evidence: A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.
A baseline model for automatic extraction of anglicisms was created using the annotated corpus we just presented as training material. As mentioned in Section 3, the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data BIBREF23, BIBREF24.
Answer: The answers are shown as follows:
* the task of detecting anglicisms can be approached as a sequence labeling problem where only certain spans of texts will be labeled as anglicism (in a similar way to an NER task). The chosen model was conditional random field model (CRF), which was also the most popular model in both Shared Tasks on Language Identification for Code-Switched Data
Question: Why is the United States Military Academy called West Point?
Answer: The United States Military Academy is called West Point because it is on the western point of an S-turn on the Hudson River.  During the American Revolution, the Revolutionary Army stretched an iron chain across the Hudson at this point because British ships would have to slow down to navigate the S-turn, making them susceptible to cannon fire.  The British never tested the chain, and their Naval forces remained separated in Canada and New York.  Due to its success on the western point of the river, the United States Military Academy has been commonly referred to as "West Point," since the military academy became the first training institution for U.S. Army officers in 1802.