DataHammer/scimrc huggingface dataset:
Question: What do they want to acquire after the word embedding step?
Evidence: In this question answering task, a reading passage , a query and several answer choices are given. P denotes the passage, Q denotes query and C denotes one of the multiple choices. The target of the model is to choose a correct answer A from multiple choices based on informations of P and Q.
Fig. FIGREF1 is the pipeline overview of QACNN. First, we use embedding layer to transform P, Q, and C into word embedding. Then the compare layer generates passage-query similarity map INLINEFORM0 and passage-choice similarity map INLINEFORM1 . The following part is the main component of QACNN. It consists of two-staged CNN architecture. The first stage projects word-level feature into sentence-level, and the second stage projects sentence-level feature into passage-level. Moreover, we apply query-based attention mechanism to each stage on the basis of INLINEFORM2 feature at word level and sentence level respectively. After QACNN Layer, we obtain each choice answer feature. Finally, a prediction layer collects output information from every choice feature and returns the most possible answer.
After word embedding step, We want to acquire similarity map which tells us location relationship between passage and query, passage and choices.
Answer: Similarity map which tells them location relationship between passage and query, passage and choices.
Question: What baselines other than standard transformers are used in experiments?
Evidence: An attention-based sequence-to-sequence model with the emoji vector as additional input as discribed in MojiTalk BIBREF16.
An RNN-based conditional variational autoencoder for dialogue response generation BIBREF16, which uses a multivariate Gaussian latent variable to model the response and concatenate it with the last hidden state of the encoder as the initial state of the decoder. KL annealing, early stopping strategy and bag-of-word auxiliary loss are applied during the training. We use the implementation released by BIBREF16.
An attention-based sequence-to-sequence model with the emoji vector as additional input as discribed in MojiTalk BIBREF16.
CVAE.
An RNN-based conditional variational autoencoder for dialogue response generation BIBREF16, which uses a multivariate Gaussian latent variable to model the response and concatenate it with the last hidden state of the encoder as the initial state of the decoder. KL annealing, early stopping strategy and bag-of-word auxiliary loss are applied during the training. We use the implementation released by BIBREF16
Answer: The answers are shown as follows:
* attention-based sequence-to-sequence model 
* CVAE
Question: Besides a putter, what other golf clubs would suffice for a putting stroke?
Answer: Any low iron (1 thru 4) or fairway wood which a flat face.
Question: What is an egg laying mammal?
Evidence: The platypus (Ornithorhynchus anatinus), sometimes referred to as the duck-billed platypus, is a semiaquatic, egg-laying mammal endemic to eastern Australia, including Tasmania. The platypus is the sole living representative or monotypic taxon of its family (Ornithorhynchidae) and genus (Ornithorhynchus), though a number of related species appear in the fossil record.
Answer: Duck-billed platypus is an egg-laying mammal found in eastern Australia. It is the sole living representative in its genus Ornithorhynchus.
Question: Why do we have seasons, and what are they?
Answer: As the earth spins on its axis, producing night and day, it also moves about the sun in an elliptical (elongated circle) orbit that requires about 365 1/4 days to complete. The earth's spin axis is tilted with respect to its orbital plane. This is what causes the seasons.

There are four primary seasons: Spring, Summer, Autumn & Winter. The start dates of the seasons themselves are different depending on what hemisphere you're located in. Spring & Summer are the warmer seasons. Autumn is typically mild, and Winter is the coldest season of the year.
Question: What is SAP?
Answer: SAP is a multinational software vendor headquartered in Walldorf, Germany. SAP stands for "System Analyse und Programmentwicklung" which are the German words for System Analysis and Program Development. SAP was founded in 1972 by Dietmar Hopp, Klaus Tschira, Hans-Werner Hector, Hasso Plattner, and Claus Wellenreuther in Germany. Today SAP has 112.000 employees and generated a yearly revenue of over 30 billion Euro in 2022.
Question: How many experts in NLP and SW do the authors use to evaluate LD2NL?
Evidence: NLG is the process of automatically generating coherent NL text from non-linguistic data BIBREF0. Recently, the field has seen an increased interest in the development of NLG systems focusing on verbalizing resources from SW data BIBREF1. The SW aims to make information available on the Web easier to process for machines and humans. However, the languages underlying this vision, i.e., RDF, SPARQL and OWL, are rather difficult to understand for non-expert users. For example, while the meaning of the OWL class expression Class: Professor SubClassOf: worksAt SOME University is obvious to every SW expert, this expression (“Every professor works at a university”) is rather difficult to fathom for lay persons.
Previous works such as SPARQL2NL BIBREF2 and SPARTIQULATION BIBREF3 have already shown the usefulness of the verbalization of SPARQL and RDF in areas such as question answering BIBREF4 and the explanation of the output of systems based onSW technologies BIBREF5. However, other SW languages are rarely investigated, such as OWL.
In this paper, we present an open-source holistic NLG framework for the SW, named LD2NL, which facilitates the verbalization of the three key languages of the SW, i.e., RDF, OWL, and SPARQL into NL. Our framework is based on a bottom-up paradigm for verbalizing SW data. Additionally, LD2NL builds upon SPARQL2NL as it is open-source and the paradigm it follows can be reused and ported to RDF and OWL. Thus, LD2NL is capable of generating either a single sentence or a summary of a given resource, rule, or query. To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW. The results suggest that LD2NL generates texts which can be easily understood by humans. The version of LD2NL used in this paper, all experimental results will be publicly available.
To validate our framework, we evaluated LD2NL using experts 66 in NLP and SW as well as 20 non-experts who were lay users or non-users of SW.
Answer: 66.
Question: Which is an imperial or a metric measurement: yard, metre
Answer: yard is imperial, metre is metric
Question: How far is the village from Warsaw?
Evidence: Żabia Wola [ˈʐabja ˈvɔla] is a village in Grodzisk Mazowiecki County, Masovian Voivodeship, in east-central Poland. It is the seat of the gmina (administrative district) called Gmina Żabia Wola. It lies approximately 10 kilometres (6 mi) south-east of Grodzisk Mazowiecki and 30 km (19 mi) south-west of Warsaw.
The village has a population of 530.
Answer: The village lies approximately 30 km (19 mi) south-west of Warsaw.
Question: Classify each of the following into either a solid, liquid or gas: blood, xenon, gold, wood, neon, brick, milk, xenon, coffee, gases, helium
Answer: solid: gold, wood, brick
liquid: milk, blood, coffee
gas: helium, xenon, neon
Question: What is the difference between an electric guitar and acoustic guitar?
Answer: The main difference between an electric guitar and an acoustic guitar is the way they are played. An electric guitar has a pickup attached to it and is played with an amplifier. On the other hand, an acoustic guitar is played without an amplifier and is typically plugged into an amplifier when being played in a live setting.
Question: What kind of boat should I purchase?
Answer: There are a large variety of options in recreation boats.  You can purchase a sailboat or a power boat, and the latter can have inboard or outboard engines while a sailboat can be single or multi-masted.  You also need to decide the length you can safely operate based on your experience, the average depth of the water where you intent to use the boat, and whether you will keep it on a lift, in the water, or on a trailer.  Finally, you need to decide on your budget for both the initial purchase as well as ongoing maintenance, which can typically cost 10-20% of the purchase price based on complexity and age.
Question: Write a brief about your recent vacation
Answer: It was family time and no other place came to my mind for a relaxing time to be spent with family. The serene backwaters of the resort were the perfect destination to enjoy the long weekend. This was my daughter's first long drive and she enjoyed the outing and all the nearby vacation spots we visited. We enjoyed the tour and are looking forward to our next visit!
Question: If someone is tall, what sport should they try?
Answer: Height is known for being advantageous in basketball
Question: Given this reference text, give me a list of all the schools attended by Tripp.
Evidence: Tripp attended The Hill School at Pottstown, Pennsylvania where he drew his attention for his talent as a football player. Tripp enrolled at the University of Chicago and played at the tackle for Amos Alonzo Stagg's 1902 football team. In January 1903, Tripp transferred to Yale University, where he played guard for Yale University's football teams in 1904 and 1905 after sitting out the 1903 season. He was captain of Yale's championship team of 1905, and was selected by Walter Camp as a first-team All-American in 1905. He later worked as a stockbroker He died in October 1962.
Answer: The Hill School, University of Chicago, Yale University
Question: What is telnet?
Answer: Telnet is an application (e.g. client/server) that provides terminal access to servers, local area networks, or other computers on the same network.  In the early days telnet was a service offering through the windows operating system.  Telnet has two core components: the communication protocol and the application that offers the communication protocol service.
Question: Pick if these would be useful or not useful for a high school student to put in their backpack. Notebooks, textbook, desk lamp, pencil pouch, beach ball, pillow, laptop.
Answer: Notebooks: useful, textbook: useful, desk lamp: not useful, pencil pouch: useful, beach ball: not useful, pillow: not useful, laptop: useful.
Question: Do they have any plans for the future?
Evidence: Motivated by the growing interest in matching user account across different social media and networking sites, in this paper we presented models for Digital Stylometry, which is a method for matching users through stylometry inspired techniques. We used temporal and linguistic patterns of users to do the matching.
We experimented with linguistic, temporal, and combined temporal-linguistic models using standard and novel techniques. The methods based on our novel confusion model outperformed the more standard ones in all cases. We showed that both temporal and linguistic information are useful for matching users, with the best temporal model performing with an accuracy of $.10$ and the best linguistic model performing with an accuracy of $0.27$ . Even though the linguistic models vastly outperformed the temporal models, when combined the temporal-linguistic models outperformed both with an accuracy of $0.31$ . The improvement in the performance of the combined models suggests that although temporal information is dwarfed by linguistic information, in terms of its contribution to digital stylometry, it nonetheless provides non-overlapping information with the linguistic data.
Our models were evaluated on $5,612$ users with a total of $11,224$ accounts on Twitter and Facebook combined. In contrast to other works in this area, we did not use any profile information in our matching models. The only information that was used in our models were the time and the linguistic content of posts by the users. This is in accordance with traditional stylometry techniques (since people could lie or misstate this information). Also, we wanted to show that there are implicit clues about the identity of users in the content (language) and context (time) of the users' interactions with social networks that can be used to link their accounts across different services.
In addition to the technical contributions (such as our confusion model), we hope that this paper is able to shed light on the relative ease with which seemingly innocuous information can be used to track users across social networks, even when signing up on different services using completely different account and profile information. In the future, we hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into our models.
In the future, we hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into our models.
Answer: They hope to extend this work to other social network sites, and to incorporate more sophisticated techniques, such as topic modelling and opinion mining, into their models.
Question: What years did World War II take place?
Answer: World War II took place from 1939 to 1945.
Question: How they perform manual evaluation, what is criteria?
Evidence: Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.
Each annotator annotated 90 reference sentences (i.e. from the training corpus) with which style they thought the sentence was from. The accuracy on this baseline task for annotators A1, A2, and A3 was 80%, 88%, and 80% respectively, giving us an upper expected bound on the human evaluation.
Answer: The answers are shown as follows:
* accuracy
Question: In The Big Lebowski, The Dude can't stand which band?
Answer: The Eagles.
Question: Identify which instrument is string or percussion: Jingle, Utogardon
Answer: Utogardon is string, Jingle is percussion.
Question: What some ways you can cook a steak?
Answer: You can cook a steak a number of different ways, including pan-searing, baking, grilling, smoking, or sous vide.  Not all methods are suitable for all cuts of meat, however.  For example, leaner cuts will benefit from a hot pan sear, or sous vide, but may end up too dry if smoked.  Fattier cuts may not fair as well on a grill, due to flare ups caused by the drippings.
Question: Give me the top 5 things I should do to prepare for a holiday abroad
Answer: 1. Check any travel advice from your government
2. Consult medical advice to ensure that you receive the recommended vaccinations and medicine for your destination countries 
3. Make sure that your passport is valid and not due to expire within the next 6 months 
4. 
5. Research the language, culture and customs of your destination countries
Question: Is the model presented in the paper state of the art?
Evidence: To make a thorough empirical comparison with previous studies, Table 3 (below the dashed line) also shows the results of some state-of-the-art supervised coreference resolution systems — IMS: the second best system in the CoNLL 2012 shared task BIBREF28 ; Latent-Tree: the latent tree model BIBREF29 obtaining the best results in the shared task; Berkeley: the Berkeley system with the final feature set BIBREF12 ; LaSO: the structured perceptron system with non-local features BIBREF30 ; Latent-Strc: the latent structure system BIBREF31 ; Model-Stack: the entity-centric system with model stacking BIBREF32 ; and Non-Linear: the non-linear mention-ranking model with feature representations BIBREF33 . Our unsupervised ranking model outperforms the supervised IMS system by 1.02% on the CoNLL F1 score, and achieves competitive performance with the latent tree model. Moreover, our approach considerably narrows the gap to other supervised systems listed in Table 3 .
Our unsupervised ranking model outperforms the supervised IMS system by 1.02% on the CoNLL F1 score, and achieves competitive performance with the latent tree model. Moreover, our approach considerably narrows the gap to other supervised systems listed in Table 3 .
Answer: No, supervised models perform better for this task.
Question: How many steps can the proposed approach's training process be summarized?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
In this section, we will present a cross-lingual pretraining based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source↔ivot and pivot↔arget bilingual data but no source↔arget parallel data, and the whole training process can be summarized as follows step by step: -Pre-train a universal encoder with source/pivot monolingual or source↔pivot bilingual data. -Train a pivot→target parent model built on the pre-trained universal encoder with the available parallel data.  During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy is sue (Howard and Ruder 2018). -Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
Answer: Three steps.
Question: How many days is enough to vacation in Belgium?
Answer: Seven days are enough days to vacation in Belgium.  Recommended areas to visit are Brussels, Bruges, Ghent, Leuven, and the oceanside.
Question: Is the study based on datasets in Turkish only?
Evidence: Sentiment analysis has recently been one of the hottest topics in natural language processing (NLP). It is used to identify and categorise opinions expressed by reviewers on a topic or an entity. Sentiment analysis can be leveraged in marketing, social media analysis, and customer service. Although many studies have been conducted for sentiment analysis in widely spoken languages, this topic is still immature for Turkish and many other languages.
Neural networks outperform the conventional machine learning algorithms in most classification tasks, including sentiment analysis BIBREF0. In these networks, word embedding vectors are fed as input to overcome the data sparsity problem and make the representations of words more “meaningful” and robust. Those embeddings indicate how close the words are to each other in the vector space model (VSM).
Most of the studies utilise embeddings, such as word2vec BIBREF1, which take into account the syntactic and semantic representations of the words only. Discarding the sentimental aspects of words may lead to words of different polarities being close to each other in the VSM, if they share similar semantic and syntactic features.
For Turkish, there are only a few studies which leverage sentimental information in generating the word and document embeddings. Unlike the studies conducted for English and other widely-spoken languages, in this paper, we use the official dictionaries for this language and combine the unsupervised and supervised scores to generate a unified score for each dimension of the word embeddings in this task.
Our main contribution is to create original and effective word vectors that capture syntactic, semantic, and sentimental characteristics of words, and use all of this knowledge in generating embeddings. We also utilise the word2vec embeddings trained on a large corpus. Besides using these word embeddings, we also generate hand-crafted features on a review basis and create document vectors. We evaluate those embeddings on two datasets. The results show that we outperform the approaches which do not take into account the sentimental information. We also had better performances than other studies carried out on sentiment analysis in Turkish media. We also evaluated our novel embedding approaches on two English corpora of different genres. We outperformed the baseline approaches for this language as well. The source code and datasets are publicly available.
The paper is organised as follows. In Section 2, we present the existing works on sentiment classification. In Section 3, we describe the methods proposed in this work. The experimental results are shown and the main contributions of our proposed approach are discussed in Section 4. In Section 5, we conclude the paper.
We also evaluated our novel embedding approaches on two English corpora of different genres.
Answer: No, they also evaluated their approaches on two English corpora of different genres.
Question: Identify which instrument is string or percussion: Damphu, Dulcimer
Answer: Dulcimer is string, Damphu is percussion.
Question: What was the tagline on The X-Files episode "Anasazi"?
Answer: The tagline "Éí 'Aaníígóó 'Áhoot'é" replaced the usual tagline of "The Truth is Out There" on the season 2 episode entitled "Anasazi." The phrase translated to "the truth is out there" in Navajo.
Question: Name well known movies with George Clooney.
Answer: Well known movies with George Clooney are Up in the Air, Ocean's Eleven, Gravity, and Three Kings.
Question: Are Rainbow Trout and Steelhead the same?
Answer: Steelhead and Rainbow trout are the same species, but have different lifestyles.  Steelhead spend part of their lives in the sea before going up river to breed, while rainbow trout spend their lives in freshwater.
Question: Where was the 2nd edition of Indian and Bangladesh Navies held?
Answer: The 2nd edition of Indian and Bangladesh Navies was held in Bay of Bengal
Question: I'm really good at maths but not great at science, which of the following educational courses would I be more likely to succeed with? algebra, biology, matrices, probability, statistics, geology, and chemistry.
Answer: Algebra, matrices, probability, statistics are all maths subjects so you would be more likely to succeed at those than the science subjects.
Question: Classify each job as a "blue collar" or "white collar" job: auditor, electrician, plumber, data scientist, financial analyst, construction worker
Answer: Auditor: White Collar
Electrician: Blue Collar
Plumber: Blue Collar
Data Scientist: White Collar
Financial Analyst: White Collar
Construction Worker: Blue Collar
Question: What family do tigers, lions and panthers all belong to?
Answer: Tigers, lions and panthers are all a part of the cat family
Question: Is weightlifting good for you?
Answer: Weightlifting can be a healthy part of any fitness plan due to how it develops muscle mass across your body. This increase in muscle mass can enhance strength, speed, and explosiveness. Weightlifting has also been shown to improve many important biomarkers such as testosterone and cortisol levels.