DataHammer/scimrc huggingface dataset:
Question: What kind of RNN do they use in their experimentation?
Evidence: The power of neural networks comes from their ability to find data representations that are useful for classification. Recurrent Neural Networks (RNN) are a special type of neural network, which can be thought of as the addition of loops to the architecture. RNNs use back propagation in the training process to update the network weights in every layer. In our experimentation we used a powerful type of RNN known as Long Short-Term Memory Network (LSTM). Inspired by the work by BIBREF15 , we experiment with combining various LSTM models enhanced with a number of novel features in an ensemble. More specifically we introduce:
In our experimentation we used a powerful type of RNN known as Long Short-Term Memory Network (LSTM).
Answer: Long Short-Term Memory Network
Question: What crowdsourcing platform was used?
Evidence: We recruited 176 AMT workers to participate in our conceptualization task. Of these workers, 90 were randomly assigned to the Control group and 86 to the AUI group. These workers completed 1001 tasks: 496 tasks in the control and 505 in the AUI. All responses were gathered within a single 24-hour period during April, 2017.
We recruited 176 AMT workers to participate in our conceptualization task.
Answer: The answers are shown as follows:
* AMT
Question: Why is nitrogen gas used during the packaging of potato chips?
Answer: The Brits call them “crisps” for a reason. Three reasons at least for using 100% nitrogen:

Nitrogen contains no oxygen like air does which oxidizes the potatoes and promotes bacteria growth.
Nitrogen contains no water, which removes “crispness” from the chips and promotes bacteria growth.
Nitrogen inflates the bag which protects the chips, but, honestly, that could be done with air which costs less than pure nitrogen. But we know the downsides of air, don’t we?
Question: List some import aspects about location Ljubija in given text
Evidence: In the 19th century, Ljubija developed into one of the most important locations for the area's economy due to its iron ore deposits. After World War II Ljubija became one of the largest iron mines in the world which employed more than 5,000 people at any one time. Until 1963 it was governed as a separate municipality, before it was merged with Prijedor.
Answer: 1. Because of its iron ore resources, Ljubija became one of the most significant destinations for the region's economy in the 19th century.
2. Ljubija grew to be one of the world's largest iron mines after World War II, employing more than 5,000 people at any given moment.
3. Prior to being amalgamated with Prijedor, it was governed as an independent municipality until 1963.
Question: What is a Haiku?
Answer: A haiku is a short poem composed of three phrases and 17 syllables, with the first and last phrases being 5 syllables and the second phrase being 7 syllables. Originating from Japan, haikus are typically centered around nature-related topics, such as Spring.
Question: What are Marge's sisters named on the Simpsons
Answer: Patty & Selma
Question: What is Equity theater?
Evidence: The Actors' Equity Association (AEA), commonly called Actors' Equity or simply Equity, is an American labor union representing those who work in live theatrical performance. Performers appearing in live stage productions without a book or through-storyline (vaudeville, cabarets, circuses) may be represented by the American Guild of Variety Artists (AGVA). The AEA works to negotiate quality living conditions, livable wages, and benefits for performers and stage managers. A theater or production that is not produced and performed by AEA members may be called "non-Equity".
Answer: A theater or production that is performed by Actors' Equity Association (AEA) members is often called "Equity Theater". Actors' Equity Association commonly called Actor's Equity or more simply Equity, is an American Labor union representing those who work in live theatrical performance.  A theater or production not produced by Actors' Equity members is often called "non-Equity".
Question: Why does it help to focus on the breath when practicing mindfulness?
Answer: The art of meditation, which is another name for the practice of mindfulness, is all about being in the present.  In other words, cultivating an awareness of what is happening in the present moment.  This is done by quietening the mind of all the random thoughts that are being continuously generated, and having the senses alerted to the signals that are being passed in the moment.  The signals are the sensations or emotions or sounds that one is feeling and hearing in the present moment.  To accomplish this, the breath serves as an anchor to the present moment.  Being aware of one's breath keeps us in the present moment.  When the mind eventually wanders, the breath is a signal to tune back into the present moment.  The breath is always with us and can be found in an instant.
Question: What is the difference between walking and running?
Answer: Walking and running involves taking steps to gain velocity, typically on land.  The differences come in terms of speed.  Walking happens at a slower pace and pace than running.  One that traverses longer distances at a faster rate when running versus walking.
Question: What are the limitations of this article?
Evidence: Building effective machine learning models for text requires data and different resources such as pre-trained word embeddings and reusable lexicons. Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages.
In this paper, we studied the research question: Can we build reusable sentiment analysis models that can be utilized for making inferences in different languages without requiring separate models and resources for each language? To that end, we built a recurrent neural network model in the language that had largest data available. We took a general-to-specific model building strategy where the larger corpus that had reviews from different domains was first used to train the RNN model and a smaller single-domain corpus of sentiment reviews was used to specialize the model on the given domain. During scoring time, we used corpora for the given domain in different languages and translated them to English to be able to classify sentiments with the trained model. Experimental results showed that the proposed multilingual approach outperforms both the majority baseline and the lexicon-based baseline.
In this paper we made the sentiment analysis model specific to a single domain. For future work, we would like to investigate the effectiveness of our model on different review domains including hotel reviews and on different problems such as detecting stance.
Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages.
Answer: Unfortunately, most of these resources are not entirely transferable to different domains, tasks or languages. Sentiment analysis is one such task that requires additional effort to transfer knowledge between languages.
Question: What is a geodetic system?
Answer: A geodetic datum or geodetic system (also: geodetic reference datum, geodetic reference system, or geodetic reference frame) is a global datum reference or reference frame for precisely representing the position of locations on Earth or other planetary bodies by means of geodetic coordinates. Datums are crucial to any technology or technique based on spatial location, including geodesy, navigation, surveying, geographic information systems, remote sensing, and cartography. A horizontal datum is used to measure a location across the Earth's surface, in latitude and longitude or another coordinate system; a vertical datum is used to measure the elevation or depth relative to a standard origin, such as mean sea level (MSL). Since the rise of the global positioning system (GPS), the ellipsoid and datum WGS 84 it uses has supplanted most others in many applications. The WGS 84 is intended for global use, unlike most earlier datums.
Question: What is the input to the differential network?
Evidence: Our method is based on a sequence to sequence network BIBREF38 , BIBREF12 , BIBREF39 . The sequence to sequence network has a text sequence as input and output. In our method, we take an image as input and generate a natural question as output. The architecture for our model is shown in Figure FIGREF4 . Our model contains three main modules, (a) Representation Module that extracts multimodal features (b) Mixture Module that fuses the multimodal representation and (c) Decoder that generates question using an LSTM-based language model.
In our method, we take an image as input and generate a natural question as output.
Answer: The answers are shown as follows:
* image
Question: What is a synonym for something which is eco friendly?
Answer: Eco friendly items can also be described as being "green"
Question: Which dataset do they use?
Evidence: We used the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some. The dataset consists of 1,362 unique utterances with a noun phrase containing some (some-NP). For each example with a some-NP, degen2015investigating collected inference strength ratings from at least 10 participants recruited on Amazon's Mechanical Turk. Participants saw both the target utterance and ten utterances from the preceding discourse context. They then rated the similarity between the original utterance like (UNKREF8) and an utterance in which some was replaced with some, but not all like (UNKREF9), on a 7-point Likert scale with endpoints labeled “very different meaning” (1) and “same meaning” (7). Low similarity ratings thus indicate low inference strength, and high similarity ratings indicate high inference strength.
We used the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some. The dataset consists of 1,362 unique utterances with a noun phrase containing some (some-NP). For each example with a some-NP, degen2015investigating collected inference strength ratings from at least 10 participants recruited on Amazon's Mechanical Turk. Participants saw both the target utterance and ten utterances from the preceding discourse context. They then rated the similarity between the original utterance like (UNKREF8) and an utterance in which some was replaced with some, but not all like (UNKREF9), on a 7-point Likert scale with endpoints labeled “very different meaning” (1) and “same meaning” (7). Low similarity ratings thus indicate low inference strength, and high similarity ratings indicate high inference strength.
Answer: The answers are shown as follows:
* the annotated dataset reported by degen2015investigating, a dataset of the utterances from the Switchboard corpus of telephone dialogues BIBREF21 that contain the word some
Question: What is the JRE in Java?
Answer: The Java Runtime Environment (JRE) is software that Java programs require to run correctly. Java is a computer language that powers many current web and mobile applications. The JRE is the underlying technology that communicates between the Java program and the operating system.
Question: What happens if the sky falls down?
Answer: The question does not make sense. Sky is a term used to describe the upward view from the surface of the Earth. It can also be described as the space between Earth’s surface and outer space. Therefore the idea of the sky falling down is absurd. However, the famous idiom “The Sky is falling” refers to panic and mass hysteria.
Question: Extract a list of poems and the year they were published in the following form: {Poem name}: {Year published}

And sort the list chronologically
Evidence: Piombino's first published poems appeared in 1965 in American Weave Literary Journal. Throughout the 1970s, he continued to publish in small literary journals, such as The World, Dodgems, Telephone, and Roof.

His first volume of poetry, titled simply Poems, was published by the Sun & Moon Press in 1988 and won an Author's Recognition Award from the Postgraduate Center for Mental Health in 1992.[citation needed] His second chapbook, Light Street, did not appear until 1996. It was followed by Theoretical Objects (1999), a collection of manifestos, aphorisms, essays, and seemingly autobiographical prose poems.

Piombino's poetry took a different turn with Hegelian Honeymoon (2004), in which he moved away from the conventions of Language poetry to explore more traditional forms. Inspired by the poetry that accompanied an exhibition of Japanese calligraphy, the poems are a cross between haiku and aphorism. The poems were originally published on the SUNY/Buffalo poetics list-serve before being published in chapbook form by Chax Press.
Answer: Sure, here is a chronologically-sorted list of poems and the year they were published:

Poems: 1988
Light Street: 1996
Theoretical Objects: 1999
Hegelian Honeymoon: 2004
Question: How do the authors make the adaption for the original adversarial neural network to the setting in this work?
Evidence: Adversarial learning has been an effective mechanism to resolve the problem of the input features between the training and test examples having large divergences BIBREF27 , BIBREF13 . It has been successfully applied on domain adaption BIBREF7 , cross-lingual learning BIBREF15 and multi-task learning BIBREF17 . All settings involve feature shifting between the training and testing.
In this paper, our setting is different. We are using the annotations from non-experts, which are noise and can influence the final performances if they are not properly processed. Directly learning based on the resulting corpus may adapt the neural feature extraction into the biased annotations. In this work, we assume that individual workers have their own guidelines in mind after short training. For example, a perfect worker can annotate highly consistently with an expert, while common crowdsourcing workers may be confused and have different understandings on certain contexts. Based on the assumption, we make an adaption for the original adversarial neural network to our setting.
Our adaption is very simple. Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs, for example, the domain category in the domain adaption setting, while we add a discriminator to classify the annotation workers. Solely the features from the input sentence is not enough for worker classification. The annotation result of the worker is also required. Thus the inputs of our discriminator are different. Here we exploit both the source sentences and the crowd-annotated NE labels as basic inputs for the worker discrimination.
In the following, we describe the proposed adversarial learning module, including both the submodels and the training method. As shown by the left part of Figure 1 , the submodel consists of four parts: (1) a common Bi-LSTM over input characters; (2) an additional Bi-LSTM to encode crowd-annotated NE label sequence; (3) a convolutional neural network (CNN) to extract features for worker discriminator; (4) output and prediction.
Our adaption is very simple. Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs, for example, the domain category in the domain adaption setting, while we add a discriminator to classify the annotation workers.
Answer: Briefly speaking, the original adversarial learning adds an additional discriminator to classify the type of source inputs.
Question: What are the main ingredients for making bread?
Answer: Flour, water, yeast, and salt
Question: What are three possible phases for language formation?
Evidence: Three clear domains can be noticed in the behavior of $\langle cc \rangle $ versus $\wp $, at $t_f$, as shown in Fig. FIGREF15 (blue squares). Phase I: $\langle cc \rangle $ increases smoothly for $\wp < 0.4$, indicating that for this domain there is a small correlation between word neighborhoods. Full vocabularies are attained also for $\wp < 0.4$; Phase II: a drastic transition appears at the critical domain $\wp ^* \in (0.4,0.6)$, in which $\langle cc \rangle $ shifts abruptly towards 1. An abrupt change in $V(t_f)$ versus $\wp $ is also found (Fig. FIGREF16) for $\wp ^*$; Phase III: single-word languages dominate for $\wp > 0.6$. The maximum value of $\langle cc \rangle $ indicate that word neighborhoods are completely correlated.
Three clear domains can be noticed in the behavior of $\langle cc \rangle $ versus $\wp $, at $t_f$, as shown in Fig. FIGREF15 (blue squares). Phase I: $\langle cc \rangle $ increases smoothly for $\wp < 0.4$, indicating that for this domain there is a small correlation between word neighborhoods. Full vocabularies are attained also for $\wp < 0.4$; Phase II: a drastic transition appears at the critical domain $\wp ^* \in (0.4,0.6)$, in which $\langle cc \rangle $ shifts abruptly towards 1. An abrupt change in $V(t_f)$ versus $\wp $ is also found (Fig. FIGREF16) for $\wp ^*$; Phase III: single-word languages dominate for $\wp > 0.6$. The maximum value of $\langle cc \rangle $ indicate that word neighborhoods are completely correlated.
Answer: The answers are shown as follows:
* Phase I: $\langle cc \rangle $ increases smoothly for $\wp < 0.4$, indicating that for this domain there is a small correlation between word neighborhoods. Full vocabularies are attained also for $\wp < 0.4$
* Phase II: a drastic transition appears at the critical domain $\wp ^* \in (0.4,0.6)$, in which $\langle cc \rangle $ shifts abruptly towards 1. An abrupt change in $V(t_f)$ versus $\wp $ is also found (Fig. FIGREF16) for $\wp ^*$
* Phase III: single-word languages dominate for $\wp > 0.6$. The maximum value of $\langle cc \rangle $ indicate that word neighborhoods are completely correlated
Question: What is an acoholic?
Answer: An alcoholic is someone who is unable to control the amount of alcohol they consume.  Loss of control with any substance is how addiction starts.  Alcoholism is a form of uncontrolled addiction.  The most dangerous kind of addiction is one where a person is in denial that they have an addiction.  Alcoholism like other additions if not treated can lead to kidney failure and ultimately death in the long run.  It's best if you know someone with an alcoholic addition to try and intervene and get them help through a 12-step or alcoholics anonymous program.
Question: What are used for query expansion?
Evidence: Figure FIGREF31 shows the evaluation of our approach when discovering new informative keywords for model training (see Section SECREF2: Keyword Discovery). We compare our human-AI collaborative way of discovering new keywords against a query expansion (QE) approach BIBREF23, BIBREF24 that leverages word embeddings to find similar words in the latent semantic space. Specifically, we use pre-trained word embeddings based on a large Google News dataset for query expansion. For instance, the top keywords resulting from QE for `politician' are, `deputy',`ministry',`secretary', and `minister'. For each of these keywords, we use the crowd to label a set of tweets and obtain a corresponding expectation.
We observe that our approach consistently outperforms QE by an average of $4.62\%$ and $52.58\%$ AUC on CyberAttack and PoliticianDeath, respectively. The large gap between the performance improvements for the two datasets is mainly due to the fact that microposts that are relevant for PoliticianDeath are semantically more complex than those for CyberAttack, as they encode noun-verb relationship (e.g., “the king of ... died ...”) rather than a simple verb (e.g., “... hacked.”) for the CyberAttack microposts. QE only finds synonyms of existing keywords related to either `politician' or `death', however cannot find a meaningful keyword that fully characterizes the death of a politician. For instance, QE finds the keywords `kill' and `murder', which are semantically close to `death' but are not specifically relevant to the death of a politician. Unlike QE, our approach identifies keywords that go beyond mere synonyms and that are more directly related to the end task, i.e., discriminating event-related microposts from non related ones. Examples are `demise' and `condolence'. As a remark, we note that in Figure FIGREF31(b), the increase in QE performance on PoliticianDeath is due to the keywords `deputy' and `minister', which happen to be highly indicative of the death of a politician in our dataset; these keywords are also identified by our approach.
Specifically, we use pre-trained word embeddings based on a large Google News dataset2 for query expansion.
Answer: Pre-trained word embeddings based on a large Google News dataset2.
Question: How large is a regulation size basketball in the NBA?
Answer: The official size of the basketball used by the NBA is 29.5 inches in circumference.
Question: Show me general reviews received for game Meat Puppet using given text as a reference
Evidence: Next Generation reviewed the game, rating it three stars out of five, and stated that "Meat Puppet is the sort of game that keeps you playing just to see what happens next, so it's probably best not to give away anything else. With better control, it would have rated a star higher, no question. As it is, the gameplay is just solid enough to let you keep coming back."

Siew-Ching Goh for Australian newspaper The Age said "why set your heart on a showdown when the more likely outcome is being late for the appointment? You'll still have a lot of fun, anyway, running around with Lotos."
Answer: 1. The game received a three out of five star review from Next Generation, who also noted that "Meat Puppet is the kind of game that keeps you playing just to see what happens next, so it's probably best not to give away anything else."
2. Without a doubt, it would have received a higher rating with better control. The gameplay is currently just reliable enough to keep you coming back.
3. "Why set your heart on a showdown when the more likely outcome is being late for the appointment?," wrote Siew-Ching Goh for the Australian publication The Age. You'll still have a great time playing with Lotos, though.
Question: Is there exactly one "answer style" per dataset?
Evidence: We conducted experiments on the two tasks of MS MARCO 2.1 BIBREF5 . The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question. For instance, for the question “tablespoon in cup”, the answer in the Q&A task will be “16”, and the answer in the NLG task will be “There are 16 tablespoons in a cup.” In addition to the ALL dataset, we prepared two subsets (Table 1 ). The ANS set consists of answerable questions, and the WFA set consists of the answerable questions and well-formed answers, where WFA $\subset $ ANS $\subset $ ALL.
The answer styles considered in the experiments corresponded to the two tasks. The NLG task requires a well-formed answer that is an abstractive summary of the question and ten passages, averaging 16.6 words. The Q&A task also requires an abstractive answer but prefers a more concise answer than the NLG task, averaging 13.1 words, where many of the answers do not contain the context of the question.
Answer: Yes.