DataHammer/scimrc huggingface dataset:
Question: Do they compare the model to previously proposed architectures?
Evidence: Most of the information humanity has gathered up to this point is stored in the form of plain text. Hence the task of teaching machines how to understand this data is of utmost importance in the field of Artificial Intelligence. One way of testing the level of text understanding is simply to ask the system questions for which the answer can be inferred from the text. A well-known example of a system that could make use of a huge collection of unstructured documents to answer questions is for instance IBM's Watson system used for the Jeopardy challenge BIBREF0 .
Cloze-style questions BIBREF2 , i.e. questions formed by removing a phrase from a sentence, are an appealing form of such questions (for example see Figure FIGREF1 ). While the task is easy to evaluate, one can vary the context, the question sentence or the specific phrase missing in the question to dramatically change the task structure and difficulty.
One way of altering the task difficulty is to vary the word type being replaced, as in BIBREF3 . The complexity of such variation comes from the fact that the level of context understanding needed in order to correctly predict different types of words varies greatly. While predicting prepositions can easily be done using relatively simple models with very little context knowledge, predicting named entities requires a deeper understanding of the context.
Also, as opposed to selecting a random sentence from a text as in BIBREF3 ), the question can be formed from a specific part of the document, such as a short summary or a list of tags. Since such sentences often paraphrase in a condensed form what was said in the text, they are particularly suitable for testing text comprehension BIBREF1 .
An important property of cloze-style questions is that a large amount of such questions can be automatically generated from real world documents. This opens the task to data-hungry techniques such as deep learning. This is an advantage compared to smaller machine understanding datasets like MCTest BIBREF4 that have only hundreds of training examples and therefore the best performing systems usually rely on hand-crafted features BIBREF5 , BIBREF6 .
In the first part of this article we introduce the task at hand and the main aspects of the relevant datasets. Then we present our own model to tackle the problem. Subsequently we compare the model to previously proposed architectures and finally describe the experimental results on the performance of our model.
In the first part of this article we introduce the task at hand and the main aspects of the relevant datasets. Then we present our own model to tackle the problem. Subsequently we compare the model to previously proposed architectures and finally describe the experimental results on the performance of our model.
Answer: Yes.
Question: What languages does the dataset consist of?
Evidence: We extract a large-scale stance detection dataset from comments written by candidates of elections in Switzerland. The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection. It contains 67 000 comments on more than 150 political issues (targets). Unlike stance detection models that have specific target issues, we use the dataset to train a single model on all the issues. To make learning across targets possible, we prepend to each instance a natural question that represents the target (e.g."Do you support X?"). Baseline results from multilingual BERT show that zero-shot cross-lingual and cross-target transfer of stance detection is moderately successful with this approach.
The dataset consists of German, French and Italian text, allowing for a cross-lingual evaluation of stance detection.
Answer: German, French and Italian.
Question: Is accuracy the only metric they used to compare systems?
Evidence: In this paper, two main question answering tasks such as answer selection and answer triggering have been examined. In the answer triggering task, there is not a guarantee to have the correct answer among the list of answers. However, in answer selection, there is at least one correct answer among the candidates. As a result, answer triggering is a more challenging task. To report the result for answer selection, MAP and MRR are used; however, the answer triggering task is evaluated by F1-score. The result for MULT Method is reported in Table. 1.
To report the result for answer selection, MAP and MRR are used; however, the answer triggering task is evaluated by F1-score. The result for MULT Method is reported in Table. 1.
Answer: No.
Question: What methods or models do they compare?
Evidence: The scientific process by which the deep learning research community operates is guided by empirical studies that evaluate the relative quality of models. Complicating matters, the measured performance of a model depends not only on its architecture (and data), but it can strongly depend on hyperparameter values that affect learning, regularisation, and capacity. This hyperparameter dependence is an often inadequately controlled source of variation in experiments, which creates a risk that empirically unsound claims will be reported.
In this paper, we use a black-box hyperparameter optimisation technique to control for hyperparameter effects while comparing the relative performance of language modelling architectures based on LSTMs, Recurrent Highway Networks BIBREF0 and NAS BIBREF1 . We specify flexible, parameterised model families with the ability to adjust embedding and recurrent cell sizes for a given parameter budget and with fine grain control over regularisation and learning hyperparameters.
Once hyperparameters have been properly controlled for, we find that LSTMs outperform the more recent models, contra the published claims. Our result is therefore a demonstration that replication failures can happen due to poorly controlled hyperparameter variation, and this paper joins other recent papers in warning of the under-acknowledged existence of replication failure in deep learning BIBREF2 , BIBREF3 . However, we do show that careful controls are possible, albeit at considerable computational cost.
Several remarks can be made in light of these results. First, as (conditional) language models serve as the central building block of many tasks, including machine translation, there is little reason to expect that the problem of unreliable evaluation is unique to the tasks discussed here. However, in machine translation, carefully controlling for hyperparameter effects would be substantially more expensive because standard datasets are much larger. Second, the research community should strive for more consensus about appropriate experimental methodology that balances costs of careful experimentation with the risks associated with false claims. Finally, more attention should be paid to hyperparameter sensitivity. Models that introduce many new hyperparameters or which perform well only in narrow ranges of hyperparameter settings should be identified as such as part of standard publication practice.
In this paper, we use a black-box hyperparameter optimisation technique to control for hyperparameter effects while comparing the relative performance of language modelling architectures based on LSTMs, Recurrent Highway Networks and NAS.
Answer: Language modelling architectures based on LSTMs, Recurrent Highway Networks and NAS.
Question: what is philosophy?
Answer: Philosophy is the systematic study of general and fundamental questions about knowledge, existence, values, mind and language.
Question: What is the previous state-of-the-art model?
Evidence: Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features. Our system comfortably surpasses the existing best system at SemEval. Our system manages to improve the existing best system of SemEval 2016 task 6 by 3.2 F-score points for sentiment analysis.
We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15. Overall, our proposed system achieves an improvement of 5 F-Score points over the existing state-of-the-art system for emotion analysis. Individually, the proposed system improves the existing F-scores for all the emotions except surprise. The findings of BIBREF15 also support this behavior (i.e. worst result for the surprise class). This could be attributed to the data scarcity and a very low agreement between the annotators for the emotion surprise.
Table TABREF19 shows the comparison of our proposed system with the existing state-of-the-art system of SemEval 2016 Task 6 for the sentiment dataset. BIBREF7 used feature-based SVM, BIBREF39 used keyword rules, LitisMind relied on hashtag rules on external data, BIBREF38 utilized a combination of sentiment classifiers and rules, whereas BIBREF37 used a maximum entropy classifier with domain-specific features.
We also compare our system with the state-of-the-art systems proposed by BIBREF15 on the emotion dataset. The comparison is demonstrated in Table TABREF22. Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN were the five individual systems used by BIBREF15.
Answer: The answers are shown as follows:
* BIBREF7
* BIBREF39
* BIBREF37
* LitisMind
* Maximum entropy, SVM, LSTM, Bi-LSTM, and CNN
Question: How do I get in shape?
Answer: There are many great workout styles that can improve your physical health. For example, yoga is great for increasing blood flow, reducing stress levels in the body, and improving flexibility. If you're interested in these benefits, as well as losing weight, I recommend exploring "Hot Yoga" as a more intense variation. Another method of getting into shape is through strength training. This type of workout is focused on lifting weights to put stress on your muscles. This stress leads to micro-tears in your muscles, which heal to become even stronger than before! Some great strength training workouts include; Squats, bench press, and bicep curls. If you are looking to begin a workout regiment that you can do rather independently, running is a great option. Running leads to increased cardiovascular health and muscular strength in your legs. All that is required is a high quality pair of shoes and a sidewalk, road, or trail!
Question: Which of the following Countries are on the African Continent: Nigeria, Ethiopia, Egypt, Tanzania, Kenya, United Kingdom, Belgium, France, Germany, Russia, Afghanistan, Iraq, Brazil, Columbia.
Answer: Of the Countries you listed, Nigeria, Ethiopia, Egypt, Tanzania, and Kenya are on the African continent. 

For reference: 

United Kingdom, Belgium, France, Germany, Russia are on the European continent

Afghanistan and Iraq are on the Asian continent

Brazil and Columbia are on the South American continent
Question: How to evaluate the quality of generated texts in this paper?
Evidence: We adopted forward/reverse perplexity BIBREF33 and Self-BLEU BIBREF34 to evaluate the quality of generated texts. Forward perplexity (PPL-F) indicates the perplexity on the generated data provided by a language model trained on real data to measure the fluency of generated samples. Reverse perplexity (PPL-R) switches the roles of generated data and real data to reflect the discrepancy between the generated distribution and the data distribution. Self-BLEU (S-BLEU) regards each sentence in the generated collection as hypothesis and the others as reference to obtain BLEU scores, which evaluates the diversity of generated results.
Results are shown in Table TABREF33 . LeakGAN performs best on forward perplexity because it can generate more fluent samples. As for reverse perplexity, our model ARAML beats other baselines, showing that our model can fit the data distribution better. Other GANs, particularly LeakGAN, obtain high reverse perplexity due to mode collapse BIBREF12 , thus they only capture limited fluent expressions, resulting in large discrepancy between the generated distribution and data distribution. ARAML also outperforms the baselines in terms of Self-BLEU, indicating that our model doesn't fall into mode collapse with the help of the MLE training objective and has the ability to generate more diverse sentences.
We also provide standard deviation of each metric in Table TABREF33 , reflecting the stability of each model's performance. Our model ARAML nearly achieves the smallest standard deviation in all the metrics, indicating that our framework outperforms policy gradient in the stability of adversarial training.
We adopted forward/reverse perplexity (Zhao et al., 2018) and Self-BLEU (Zhu et al., 2018) to evaluate the quality of generated texts. 
Answer: The authors adopted forward/reverse perplexity and Self-BLEU to evaluate the quality of generated texts.
Question: From the passage provided, extract major material composition of Jupiter
Evidence: Jupiter (4.951–5.457 AU (740.7–816.4 million km; 460.2–507.3 million mi) from the Sun), at 318 MEarth, is 2.5 times the mass of all the other planets put together. It is composed largely of hydrogen and helium. Jupiter's strong internal heat creates semi-permanent features in its atmosphere, such as cloud bands and the Great Red Spot. The planet possesses a 4.2–14 Gauss strength magnetosphere that spans 22–29 million km, making it, in certain respects, the largest object in the Solar System. Jupiter has 95 known satellites. The four largest, Ganymede, Callisto, Io, and Europa, are called the Galilean moons: they show similarities to the terrestrial planets, such as volcanism and internal heating. Ganymede, the largest satellite in the Solar System, is larger than Mercury; Callisto is almost as large.
Answer: Jupiter is majorly composed of hydrogen and helium
Question: Which is the largest American Football stadium
Evidence: NRG Stadium, formerly Reliant Stadium, is a multi-purpose stadium in Houston, Texas, United States. Construction was completed in 2002, at a cost of $352 million and has a seating capacity of 72,220. It was the first NFL facility to have a retractable roof.

The stadium is the home of the National Football League's Houston Texans, the Houston Livestock Show and Rodeo, the Texas Bowl, many of the United States men's national soccer team's matches, Mexico national football team friendlies where El Tri serves as the host, and other events. The stadium served as the host facility for Super Bowls XXXVIII (2004) and LI (2017), and WrestleMania 25 (2009). The stadium will host multiple matches during the 2026 FIFA World Cup.

NRG Stadium is part of a collection of venues (including the Astrodome), which are collectively called NRG Park. The entire complex is named for NRG Energy under a 32-year, $300 million naming rights deal in 2000.
Answer: The NRG Stadium, formerly Reliant Stadium, is a multi-purpose stadium in Houston, Texas, United States. Construction was completed in 2002, at a cost of $352 million and has a seating capacity of 72,220. It was the first NFL facility to have a retractable roof.

The stadium is the home of the National Football League's Houston Texans, the Houston Livestock Show and Rodeo, the Texas Bowl, many of the United States men's national soccer team's matches, Mexico national football team friendlies where El Tri serves as the host, and other events. The stadium served as the host facility for Super Bowls XXXVIII (2004) and LI (2017), and WrestleMania 25 (2009). The stadium will host multiple matches during the 2026 FIFA World Cup.

NRG Stadium is part of a collection of venues (including the Astrodome), which are collectively called NRG Park. The entire complex is named for NRG Energy under a 32-year, $300 million naming rights deal in 2000.
Question: Identify which instrument is string or percussion: Jew's Harp, Bass Stick
Answer: Bass Stick is string, Jew's Harp is percussion.
Question: What is the main contribution of the paper? 
Evidence: All the present neural LID methods are based on acoustic features, e.g., Mel filter banks (Fbanks) or Mel frequency cepstral coefficients (MFCCs), with phonetic information largely overlooked. This may have significantly hindered the performance of neural LID. Intuitively, it is a long-standing hypothesis that languages can be discriminated between by phonetic properties, either distributional or temporal; additionally, phonetic features represent information at a higher level than acoustic features, and so are more invariant with respect to noise and channels. Pragmatically, it has been demonstrated that phonetic information, either in the form of phone sequences, phone posteriors, or phonetic bottleneck features, can significantly improve LID accuracy in both the conventional PRLM approach BIBREF11 and the more modern i-vector system BIBREF34 , BIBREF35 , BIBREF36 . In this paper, we will investigate the utilization of phonetic information to improve neural LID. The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination. This property was historically widely and successfully applied in token-based approaches, e.g., PRLM BIBREF11 , but has been largely overlooked due to the popularity of the i-vector approach.
The basic concept is to use a phone-discriminative model to produce frame-level phonetic features, and then use these features to enhance RNN LID systems that were originally built with raw acoustic features. The initial step is therefore feature combination, with the phonetic feature used as auxiliary information to assist acoustic RNN LID. This is improved further, as additional research identified that a simpler model using only the phonetic feature as the RNN LID input provides even better performance. We call this RNN model based on phonetic features the phonetic temporal neural LID approach, or PTN LID. As well as having a simplified model structure, the PTN offers deeper insight into the LID task by rediscovering the value of the phonetic temporal property in language discrimination. 
Answer: Proposing an improved RNN model, the phonetic temporal neural LID approach, based on phonetic features that results in better performance
Question: From a financial perspective, tell me five things should be considered when retiring.
Answer: Here are five financially-related things to consider when retiring:
- How much money will be spent annually while retired
- The amount of money currently saved for retirement
- Probable income from pension and/or social security
- Probable number of years until death
- Current state of the financial markets
Question: What were the Gorgons of Greek mythology?
Answer: Gorgons, female monsters depicted as having snakes on their head instead of hair, and sometimes described as having tusks, wings, and brazen claws.

- Euryale, whose scream could kill.
- Medusa, whose gaze could turn anyone to stone, was killed by Perseus.
- Stheno, the third gorgon sister.
Question: What is the research of the paper?
Evidence: Meta-data from photo-sharing websites such as Flickr can be used to obtain rich bag-of-words descriptions of geographic locations, which have proven valuable, among others, for modelling and predicting ecological features. One important insight from previous work is that the descriptions obtained from Flickr tend to be complementary to the structured information that is available from traditional scientific resources. To better integrate these two diverse sources of information, in this paper we consider a method for learning vector space embeddings of geographic locations. We show experimentally that this method improves on existing approaches, especially in cases where structured information is available.
Keywords: Social media · Text mining · Vector space embeddings · Volunteered Geographic Information · Biodiversity · Ecology.
Answer: Text mining.
Question: I want to develop OKRs for my team at work. What process should I use?
Answer: Developing OKRs for your team is a useful way to identify what goals your team will focus on and provide specific, time-bound metrics you can use to measure your success. First, identify the most important objectives for your organization - usually no more than five. Next, identify your Key Results. These Key Results should follow the SMART principles. They should be Specific, Measurable, Achievable, Relevant, and Time-bound. Once you have identified both your objectives and key results, share them with your team for their input. Once you have collected that input, finalize your OKRs and roll them out to the team through the appropriate communications channel.
Question: Why did Georges think that Helene redirected her affections towards Walter Rogers?
Evidence: Papa Chibou (Paul Muni), the elderly caretaker of Musée Pratouchy, a Parisian wax museum, feels a strong kinship with the figures, particularly with that of Napoleon. He spots at the waxworks a romantic young couple, Georges (Russell Gleason), a lawyer, and Helene (Marguerite Churchill), the daughter of a stern judge (Lester Lonergan) who disapproves of his daughter's choice and forbids her to see Georges. Papa Chibou suggests to them that they can still stay in touch, without disobeying her father's directive not to speak with each other, by placing secret personal messages in the pockets of Napoleon's uniform. However, a missing letter and confusion in communication causes Georges to arrive at the mistaken conclusion that Helene has redirected her affections towards a foolish young man (Walter Rogers), who is unworthy of her and excessively preoccupied with his stylish personal appearance and elegant clothing.
Answer: A missing letter and confusion in communication caused Georges to arrive at the mistaken conclusion that Helene has redirected her affections towards Walter Rogers (a foolish young man), who is unworthy of her and excessively preoccupied with his stylish personal appearance and elegant clothing.