DataHammer/scimrc huggingface dataset:
Question: What are the main content of the Results and discussions section?
Evidence: We report and discuss the results of our two tasks in four subsections. The first and second subsections include the effect of local representation and distant representation, respectively. The impact of CVT is explained in the third subsection. The last subsection presents a comparison of our model and all the baselines. Moreover, we also conduct paired t-tests to investigate the significance of the improvement from each contribution, as shown in Section SECREF9 .
The first and second subsections include the effect of local representation and distant representation, respectively. The impact of CVT is explained in the third subsection. The last subsection presents a comparison of our model and all the baselines. Moreover, we also conduct paired t-tests to investigate the significance of the improvement from each contribution,
Answer: The first and second subsections include the effect of local representation and distant representation, respectively. The impact of CVT is explained in the third subsection. The last subsection presents a comparison of their model and all the baselines. Moreover, they also conduct paired t-tests to investigate the significance of the improvement from each contribution.
Question: What is the novel dataset presented by authors used for?
Evidence: In this paper, we presented a novel semi-supervised pipeline along with a novel dataset for identification of complaint based posts in the transport domain. The proposed methodology can be expanded for other fields by altering the lexicons used for the creation of information cues. There are limitations to this analysis; we do not use neural networks which mandate a large volume of data. In the future, we aim to identify demographic features for identification of complaint based posts on social media platforms.
In this paper, we presented a novel semi-supervised pipeline along with a novel dataset for identification of complaint based posts in the transport domain. 
Answer: It is used for identification of complaint based posts in the transport domain.
Question: When they say "comparable performance", how much of a performance drop do these new embeddings result in?
Evidence: FLOAT SELECTED: Table 1: Results on test data. The proposed method significantly improves interpretability while maintaining comparable performance on KG tasks (Section 4.3).
FLOAT SELECTED: Table 1: Results on test data. The proposed method significantly improves interpretability while maintaining comparable performance on KG tasks (Section 4.3).
Answer: Performance was comparable, with the proposed method quite close and sometimes exceeding performance of baseline method.
Question: Does the DMN+ model establish state-of-the-art ?
Evidence: We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains.
Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains.
Answer: Yes.
Question: What approach does this work propose for the new task?
Evidence: We propose a listening comprehension model for the task defined above, the Attention-based Multi-hop Recurrent Neural Network (AMRNN) framework, and show that this model is able to perform reasonably well for the task. In the proposed approach, the audio of the stories is first transcribed into text by ASR, and the proposed model is developed to process the transcriptions for selecting the correct answer out of 4 choices given the question. The initial experiments showed that the proposed model achieves encouraging scores on the TOEFL listening comprehension test. The attention-mechanism proposed in this paper can be applied on either word or sentence levels. We found that sentence-level attention achieved better results on the manual transcriptions without ASR errors, but word-level attention outperformed the sentence-level on ASR transcriptions with errors.
We propose a listening comprehension model for the task defined above, the Attention-based Multi-hop Recurrent Neural Network (AMRNN) framework, and show that this model is able to perform reasonably well for the task. In the proposed approach, the audio of the stories is first transcribed into text by ASR, and the proposed model is developed to process the transcriptions for selecting the correct answer out of 4 choices given the question. 
Answer: The answers are shown as follows:
* We propose a listening comprehension model for the task defined above, the Attention-based Multi-hop Recurrent Neural Network (AMRNN) framework, and show that this model is able to perform reasonably well for the task. In the proposed approach, the audio of the stories is first transcribed into text by ASR, and the proposed model is developed to process the transcriptions for selecting the correct answer out of 4 choices given the question.
Question: Who is the company Adecco
Evidence: The Adecco Group, is a Swiss–French company based in Zurich, Switzerland. It is the world's second largest Human Resources provider and temporary staffing firm, and a Fortune Global 500 company.

The Adecco Group global headquarters in Zurich is located in Bellerivestrasse since 2017
The Adecco Group global headquarters in Zurich is located in Bellerivestrasse since 2017
They directly employ 700,000 people a day, and with the expansion of its operations to China, the number of people working under them totals up to 3.5 million.
Answer: The Adecco Group, is a Swiss–French company based in Zurich, Switzerland. It is the world's second largest Human Resources provider and temporary staffing firm, and a Fortune Global 500 company.
Question: How are the main international development topics that states raise identified?
Evidence: We assess the optimal number of topics that need to be specified for the STM analysis. We follow the recommendations of the original STM paper and focus on exclusivity and semantic coherence measures. BIBREF5 propose semantic coherence measure, which is closely related to point-wise mutual information measure posited by BIBREF6 to evaluate topic quality. BIBREF5 show that semantic coherence corresponds to expert judgments and more general human judgments in Amazon's Mechanical Turk experiments.
Exclusivity scores for each topic follows BIBREF7 . Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. Cohesive and exclusive topics are more semantically useful. Following BIBREF8 we generate a set of candidate models ranging between 3 and 50 topics. We then plot the exclusivity and semantic coherence (numbers closer to 0 indicate higher coherence), with a linear regression overlaid (Figure FIGREF3 ). Models above the regression line have a “better” exclusivity-semantic coherence trade off. We select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivity at the same level of semantic coherence. The topic quality is usually evaluated by highest probability words, which is presented in Figure FIGREF4 .
We assess the optimal number of topics that need to be specified for the STM analysis. We follow the recommendations of the original STM paper and focus on exclusivity and semantic coherence measures.
Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. 
Following BIBREF8 we generate a set of candidate models ranging between 3 and 50 topics. We then plot the exclusivity and semantic coherence (numbers closer to 0 indicate higher coherence), with a linear regression overlaid (Figure FIGREF3 ). Models above the regression line have a “better” exclusivity-semantic coherence trade off. We select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivity at the same level of semantic coherence. The topic quality is usually evaluated by highest probability words, which is presented in Figure FIGREF4 .
Answer:  They focus on exclusivity and semantic coherence measures: Highly frequent words in a given topic that do not appear very often in other topics are viewed as making that topic exclusive. They select select the 16-topic model, which has the largest positive residual in the regression fit, and provides higher exclusivity at the same level of semantic coherence.
Question: Extract all the place names from the paragraph. Different places should be separated by comma.
Evidence: The Summer Olympic Games (French: Jeux olympiques d'été), also known as the Games of the Olympiad, and often referred to as the Summer Olympics, is a major international multi-sport event normally held once every four years. The inaugural Games took place in 1896 in Athens, Greece, and the most recent Games were held in 2021 in Tokyo, Japan. The International Olympic Committee (IOC) is responsible for organising the Games and for overseeing the host city's preparations. The tradition of awarding medals began in 1904; in each Olympic event, gold medals are awarded for first place, silver medals for second place, and bronze medals for third place. The Winter Olympic Games were created out of the success of the Summer Olympic Games, which are regarded as the largest and most prestigious multi-sport international event in the world.
Answer: Athens, Greece, Tokyo, Japan
Question: Tell me some important points about AICF from the given passage.
Evidence: The All India Carrom Federation (AICF) (Hindi: अखिल भारतीय कैरम फेडरेशन) is India's national sport federation for the indigenous game of carrom, sanctioning six to seven national-level tournaments per year. It is India's representative body in the International Carrom Federation. The AICF has 15 regional and 28 state subnational affiliate institutions, the largest of which is the Maharashtra Carrom Association, further subdivided into local organisations.
Answer: 1. India's national sports federation for the traditional game of carrom, the All India Carrom Federation (AICF), annually approves six to seven national-level competitions.
2. It serves as the International Carrom Federation's representative for India.
3. The AICF has 28 state-level affiliate institutions, 15 regional affiliate institutions, and 15 local affiliate institutions, the largest of which is the Maharashtra Carrom Association.
Question: Given this paragraph about Novak Djokovic, how many majors titles does he currently hold?
Evidence: Novak Djokovic (Serbian Cyrillic: Новак Ђоковић, romanized: Novak Đoković, pronounced [nôʋaːk dʑôːkoʋitɕ] (listen); born 22 May 1987) is a Serbian professional tennis player. He is currently ranked the world No. 1 player in men's singles by the Association of Tennis Professionals (ATP), and has held the top position for a record total 381 weeks, finishing as the year-end No. 1 an Open Era record seven times. He has won a total of 93 singles titles on the men's ATP Tour with titles comprising a joint-record 22 majors, a record ten Australian Opens, a record 38 Masters, a joint-record six Tour Finals, and a record 66 Big Titles. Djokovic has completed a non-calendar year Grand Slam in singles, becoming the only man in tennis history to be the reigning champion of the four majors at once across three different surfaces. He is also the first man in the Open Era to achieve a double Career Grand Slam in singles and the only player to complete the career Golden Masters in singles by winning all of the nine Masters tournaments, which he has done twice.
Answer: 22
Question: Which of these are Satellite and Planets: Hydra, Venus, Titan, Kerberos, Pluto, Jupiter, Moon
Answer: Satellite: Kerberos, Hydra, Moon, Titan
Planets: Pluto, Jupiter, Venus
Question: How many people are needed to perform the Turing test?
Evidence: The Turing test, originally called the imitation game by Alan Turing in 1950, is a test of a machine's ability to exhibit intelligent behaviour equivalent to, or indistinguishable from, that of a human. Turing proposed that a human evaluator would judge natural language conversations between a human and a machine designed to generate human-like responses. The evaluator would be aware that one of the two partners in conversation was a machine, and all participants would be separated from one another. The conversation would be limited to a text-only channel, such as a computer keyboard and screen, so the result would not depend on the machine's ability to render words as speech. If the evaluator could not reliably tell the machine from the human, the machine would be said to have passed the test. The test results would not depend on the machine's ability to give correct answers to questions, only on how closely its answers resembled those a human would give.
Answer: You need two people: one person is the conversation partner to the machine. And another, the evaluator which observes the conversation from distance unaware which side is the machine and which side is human.
Question: Did the authors use crowdsourcing platforms?
Evidence: We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk). We use the MTurk interface of ParlAI BIBREF6 to render 360 images via WebGL and dynamically display neighborhood maps with an HTML5 canvas. Detailed task instructions, which were also given to our workers before they started their task, are shown in Appendix SECREF15 . We paired Turkers at random and let them alternate between the tourist and guide role across different HITs.
We crowd-sourced the collection of the dataset on Amazon Mechanical Turk (MTurk).
Answer: Yes.
Question: Which TV show from the 80s and 90s features a bar owned by a former baseball player?
Answer: Cheers
Question: How does the parameter-free model work?
Evidence: Table TABREF2 specifies the formulation of each model. Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naïve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$.
A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;
A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
 Note that we adopt a parsimonious design principle in our modelling: both Centroid and Naïve Bayes are parameter-free models, $k$NN only depends on the choice of $k$, and KDE uses a single bandwidth parameter $h$.
A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;
A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
Answer: The answers are shown as follows:
* A Centroid model summarizes each set of seed words by its expected vector in embedding space, and classifies concepts into the class of closest expected embedding in Euclidean distance following a softmax rule;
* A Naïve Bayes model considers both mean and variance, under the assumption of independence among embedding dimensions, by fitting a normal distribution with mean vector and diagonal covariance matrix to the set of seed words of each class;
Question: What state of the art models are used in the experiments?
Evidence: We used four classification algorithms: 1) Logistic regression, which is conventionally used in sentiment classification. Other three algorithms which are relatively new and has shown great results on sentiment classification types of problems are: 2) Naïve Bayes with SVM (NBSVM), 3) Extreme Gradient Boosting (XGBoost) and 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM).
We used four classification algorithms: 1) Logistic regression, which is conventionally used in sentiment classification. Other three algorithms which are relatively new and has shown great results on sentiment classification types of problems are: 2) Naïve Bayes with SVM (NBSVM), 3) Extreme Gradient Boosting (XGBoost) and 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM).
Answer: The answers are shown as follows:
* 2) Naïve Bayes with SVM (NBSVM)
* 3) Extreme Gradient Boosting (XGBoost)
* 4) FastText algorithm with Bidirectional LSTM (FastText-BiLSTM)
Question: What are the most popular smart ring bells?
Answer: Ring is a smart ring bell company that can record events and ping your phone when someone rings your Ring doorbell. The company has other products such as floodlights that are motion activated.
Question: Extract the most recent owner of the Seattle Storm from this Wikipedia article.
Evidence: Owners
Further information: Category:Seattle Storm owners
Barry and Ginger Ackerley, owners of the Seattle SuperSonics (2000–2001)
Howard Schultz, owner of the Seattle SuperSonics (2001–2006) 
Clay Bennett, owner of the Seattle SuperSonics (2007) 
Force 10 Hoops LLC, composed of Dawn Trudeau, Lisa Brummel, Ginny Gilder (2008–present)
Answer: The current owner of the Seattle Storm is Force
Question: Identify which instrument is string or percussion: Ravanne, Sursingar
Answer: Sursingar is string, Ravanne is percussion.
Question: What is freedom?
Answer: Freedom could mean something different for every person in the world. In this time and place I think of freedom as the ability to make my own decisions.
Question: How can I ask my aging father-in-law to see the cardiologist when he doesn't want to go?
Answer: Papa, I was concerned yesterday when you started getting shortness of breath and your chest started to hurt you.  It made me really concerned for you.

Papa, I want to tell you something, I know your daughter would want to say to you, but it is really hard for her to say.  You are a strong man.  Your daughter has always seen you as a strong man.  Asking you to do something you wouldn't want to do is something she couldn't picture herself asking you to do.  Yesterday, she wanted to beg you to go see the cardiologist to check on your heart.  She knows you hate to go to the doctor.

She didn't want you to see the cardiologist because she thinks you are no longer a strong man.  It is because she loves you.  She wants you around for many years.  She wants you to be part of seeing our kids grow up.  She wants you to come to your grandkids sporting events.  

As your son-in-law, I am asking you to come with me to the cardiologist.  I'll respect your privacy.  I can come in with you or I can wait out in the family room.  You can take it one step at a time.  They will give you a few tests.  You'll get the results.  The doctor will talk to you about the results and discuss any recommendations they may have.  It is in your control to take those recommendations.  You will never loose control.

What do you think, Papa.  Can I take you to the doctor?
Question: Which animals are a dog or cat?  a cocker spaniel, labrador, Persian, Maine Coone, and king Charles?
Answer: A cocker spaniel, labrador and king charles are all dogs. A Persian and Maine Coone are cats.
Question: Extract from the text the method encouraged to use in Apache Spark.
Evidence: Apache Spark has its architectural foundation in the resilient distributed dataset (RDD), a read-only multiset of data items distributed over a cluster of machines, that is maintained in a fault-tolerant way. The Dataframe API was released as an abstraction on top of the RDD, followed by the Dataset API. In Spark 1.x, the RDD was the primary application programming interface (API), but as of Spark 2.x use of the Dataset API is encouraged even though the RDD API is not deprecated. The RDD technology still underlies the Dataset API.
Answer: Dataset API
Question: Give me a list of the best albums of all-time
Answer: Loveless - My Bloody Valentine, Kid A - RadioHead, Souvlaki - Slowdive
Question: What are some modes of transportation I can use to travel downtown?
Answer: It depends on what city you are in. In general, the following modes of transportation are available in most downtowns:

- Taxi/Rideshare
- Bus
- Bicycle rental
- Scooter rental
- Moped rental
- Pedicab
- Walking
Question: What social media platform is observed?
Evidence: Having this in mind and if we draw from the premise that when a discussion has a high controversy it is in general due to the presence of two principal communities fighting each other (or, conversely, that when there is no controversy there is just one principal community the members of which share a common point of view), we can measure the controversy by detecting if the discussion has one or two principal jargons in use. Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set “who is with who". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.
Our method is tested on Twitter datasets. This microblogging platform has been widely used to analyze discussions and polarization BIBREF12, BIBREF13, BIBREF14, BIBREF15, BIBREF2. It is a natural choice for these kind of problems, as it represents one of the main fora for public debate in online social media BIBREF15, it is a common destination for affiliative expressions BIBREF16 and is often used to report and read news about current events BIBREF17. An extra advantage of Twitter for this kind of studies is the availability of real-time data generated by millions of users. Other social media platforms offer similar data-sharing services, but few can match the amount of data and the accompanied documentation provided by Twitter. One last asset of Twitter for our work is given by retweets, whom typically indicate endorsement BIBREF18 and hence become a useful concept to model discussions as we can set “who is with who". However, our method has a general approach and it could be used a priori in any social network. In this work we report excellent result tested on Twitter but in future work we are going to test it in other social networks.
Answer: The answers are shown as follows:
* Twitter
Question: Write a poem about George Washington.
Answer: George Washington was a British man
The British had laws he couldn't understand
Washington and his friends put together a plan
That the people would resist and America could stand
Question: Booker Prize 2021 was given to Damon Galgut for
Answer: The Promise