DataHammer/scimrc huggingface dataset:
Question: What is ruled out by the Gulordava et al. (2018) and Marvin and Linzen (2018) conditions?
Evidence: The BERT models perform remarkably well on all the syntactic test cases. I expected the attention-based mechanism to fail on these (compared to the LSTM-based models), and am surprised by these results. The BIBREF2 and BIBREF3 conditions rule out the possibility of overly relying on selectional preference cues or memorizing the wikipedia training data, and suggest real syntactic generalization is taking place. Exploring the extent to which deep purely-attention-based architectures such as BERT are capable of capturing hierarchy-sensitive and syntactic dependencies—as well as the mechanisms by which this is achieved—is a fascinating area for future research.
The Gulordava et al. (2018) and Marvin and Linzen (2018) conditions rule out the possibility of overly relying on selectional preference cues or memorizing the wikipedia training data, and suggest real syntactic generalization is taking place.
Answer: The possibility of overly relying on selectional preference cues or memorizing the wikipedia training data.
Question: When categorizing walnuts, pistachios, peanuts, hazelnuts, macadamia nuts, almonds, lentils and chickpeas, which would you say are nuts and which are legumes?
Answer: Walnuts, pistachios, hazelnuts, macadamia nuts, cashews, and almonds are all nuts. Peanuts, lentils and chickpeas are legumes.
Question: List some of the games that improve brain function.
Answer: Sudoku, Lumosity, Crosswords, Elevate, Peak, Happy Neuron, Braingle, Queendom
Question: What areas can Neural language models be used in?
Evidence: Neural language models BIBREF0 , BIBREF1 , BIBREF2 have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning. They have also become a common benchmarking application in machine learning research on recurrent neural networks (RNN), because producing an accurate probabilistic model of human language is a very challenging task which requires all levels of linguistic analysis, from pragmatics to phonology, to be taken into account.
A typical language model is trained on text in a single language, and if one needs to model multiple languages the standard solution is to train a separate model for each language. This presupposes large quantities of monolingual data in each of the languages that needs to be covered and each model with its parameters is completely independent of any of the other models.
We propose instead to use a single model with real-valued vectors to indicate the language used, and to train this model with a large number of languages. We thus get a language model whose predictive distribution INLINEFORM0 is a continuous function of the language vector INLINEFORM1 , a property that is trivially extended to other neural NLP models. In this paper, we explore the “language space” containing these vectors, and in particular explore what happens when we move beyond the points representing the languages of the training corpus.
The motivation of combining languages into one single model is at least two-fold: First of all, languages are related and share many features and properties, a fact that is ignored when using independent models. The second motivation is data sparseness, an issue that heavily influences the reliability of data-driven models. Resources are scarce for most languages in the world (and also for most domains in otherwise well-supported languages), which makes it hard to train reasonable parameters. By combining data from many languages, we hope to mitigate this issue.
In contrast to related work, we focus on massively multilingual data sets to cover for the first time a substantial amount of the linguistic diversity in the world in a project related to data-driven language modeling. We do not presuppose any prior knowledge about language similarities and evolution and let the model discover relations on its own purely by looking at the data. The only supervision that is giving during training is a language identifier as a one-hot encoding. From that and the actual training examples, the system learns dense vector representations for each language included in our data set along with the character-level RNN parameters of the language model itself.
Neural language models (Bengio et al., 2003; Mikolov et al., 2010; Sundermeyer et al., 2012) have become an essential component in several areas of natural language processing (NLP), such as machine translation, speech recognition and image captioning.
Answer: Machine translation, speech recognition and image captioning.
Question: What is the performance of model ARAML in the stability of adversarial training?
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.
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.
Answer: The model ARAML nearly achieves the smallest standard deviation in all the metrics, indicating that the framework outperforms policy gradient in the stability of adversarial training.
Question: Tell me if these are examples of fine art mediums: painting, sculpture, architecture, music, poetry
Answer: Yes, traditionally the mediums of painting, sculpture, architecture, music, and poetry are all examples of fine art. In modern times, the categorization of fine arts also includes film, video, and photography.
Question: What is their proposed Yedda?
Evidence: Natural Language Processing (NLP) systems rely on large-scale training data BIBREF0 for supervised training. However, manual annotation can be time-consuming and expensive. Despite detailed annotation standards and rules, inter-annotator disagreement is inevitable because of human mistakes, language phenomena which are not covered by the annotation rules and the ambiguity of language itself BIBREF1 .
Existing annotation tools BIBREF2 , BIBREF3 , BIBREF4 , BIBREF5 mainly focus on providing a visual interface for user annotation process but rarely consider the post-annotation quality analysis, which is necessary due to the inter-annotator disagreement. In addition to the annotation quality, efficiency is also critical in large-scale annotation task, while being relatively less addressed in existing annotation tools BIBREF6 , BIBREF7 . Besides, many tools BIBREF6 , BIBREF4 require a complex system configuration on either local device or server, which is not friendly to new users.
To address the challenges above, we propose Yedda , a lightweight and efficient annotation tool for text span annotation. A snapshot is shown in Figure FIGREF4 . Here text span boundaries are selected and assigned with a label, which can be useful for Named Entity Recognition (NER) BIBREF8 , word segmentation BIBREF9 , chunking BIBREF10 ,etc. To keep annotation efficient and accurate, Yedda provides systematic solutions across the whole annotation process, which includes the shortcut annotation, batch annotation with a command line, intelligent recommendation, format exporting and administrator evaluation/analysis.
Figure FIGREF1 shows the general framework of Yedda. It offers annotators with a simple and efficient Graphical User Interface (GUI) to annotate raw text. For the administrator, it provides two useful toolkits to evaluate multi-annotated text and generate detailed comparison report for annotator pair. Yedda has the advantages of being:
• INLINEFORM0 Convenient: it is lightweight with an intuitive interface and does not rely on specific operating systems or pre-installed packages.
• INLINEFORM0 Efficient: it supports both shortcut and command line annotation models to accelerate the annotating process.
• INLINEFORM0 Intelligent: it offers user with real-time system suggestions to avoid duplicated annotation.
• INLINEFORM0 Comprehensive: it integrates useful toolkits to give the statistical index of analyzing multi-user annotation results and generate detailed content comparison for annotation pairs.
This paper is organized as follows: Section 2 gives an overview of previous text annotation tools and the comparison with ours. Section 3 describes the architecture of Yedda and its detail functions. Section 4 shows the efficiency comparison results of different annotation tools. Finally, Section 5 concludes this paper and give the future plans.
To address the challenges above, we propose YEDDA1 , a lightweight and efficient annotation tool for text span annotation.
Answer: A lightweight and efficient annotation tool for text span annotation.
Question: How can the Federal Reserve control inflation?
Answer: The Federal Reserve has a "dual mandate" to both keep prices stable and maintain the highest level of sustainable employment. Controlling inflation falls into the scope of price stability. The Fed has three main tools to achieve this goal. First, they can change the Federal Funds Rate which is the interest rate used by banks for inter-bank lending and is the foundation for interest rates more broadly. An increase in the Federal Funds Rate, will cause interest rates to increase for loans, mortgages, credit cards, etc. The higher costs of interest rates cause prices to decrease. Second, the Fed can set expectations for path of the Federal Funds Rate via announcements from Federal Open Market Committee (FOMC) and "Fed Speak" from individual Fed governors. These updates and commentary are based on the Feds marcoeconcomic forecasts and the individual opinions of the Fed governors. These expectations can influence the publics expectation for inflation and therefore the actual path of inflation. Lastly, the Fed has many technical tools for influencing credit markets and the interest rates on government bonds. These tools involve buying/selling US treasuries or providing assurances to select credit markets. These actions can tighten/loosen these credit markets and therfore indirectly influence actual interest rates. In summary, all three tools used by the Fed play a part in the strategy for controlling inflation.
Question: What is interesting about the play Polish Bethlehem?
Evidence: Lucjan Rydel, also known as Lucjan Antoni Feliks Rydel (17 May 1870 in Kraków – 8 April 1918 in Bronowice Małe), was a Polish playwright and poet from the Young Poland movement.

Life
Rydel was the son of Lucjan Rydel, a surgeon, ophthalmologist, professor and Rector of Jagiellonian University in Kraków, and of Helena Kremer.

In 1904, Rydel wrote a nativity play, Polish Bethlehem (Betlejem polskie), and staged its production in two suburbs of Kraków, Tonie and Bronowice, with local villagers as actors. It was an expression of his profound respect for rural Poland as well as the result of his flair for theatrical experimentation. Rydel left the third and the final act of his play open. In the course of history, new characters, including contemporary Polish politicians and celebrities, were added to it by various producers to make the play appeal to new audiences.
Answer: The play Polish Bethlehem, written in 1904, has the third act open to future additions, enabling future producers to add new characters to the play to update it for contemporary times.
Question: What is the exact performance on SQUAD?
Evidence: Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling).
Table 1 shows the official leaderboard on SQuAD test set when we submitted our system. Our model achieves a 68.73% EM score and 77.39% F1 score, which is ranked among the state of the art single models (without model ensembling).
Answer: The answers are shown as follows:
* Our model achieves a 68.73% EM score and 77.39% F1 score
Question: With given text as a reference, Please jot down some details about Central Michigan University
Evidence: Central Michigan University (CMU) is a public research university in Mount Pleasant, Michigan. Established in 1892 as the Central Michigan Normal School and Business Institute, the private normal school became a state institution and renamed Central State Normal School in 1895 after the Michigan State Board of Education took over governance of the school. The institution came into its own as a university and gained its current name Central Michigan University in 1959 under the university's 6th president Judson W. Foust.
Answer: 1. Located in Mount Pleasant, Michigan, Central Michigan University (CMU) is a public research university.
2. The private normal school, which had been founded in 1892 as the Central Michigan Normal School and Business Institute, changed its name to Central State Normal School in 1895 after the Michigan State Board of Education assumed control of the institution's management.
3. Under the leadership of Judson W. Foust, the university's sixth president, the institution began to take shape as a university and adopted its present name in 1959.
Question: What is the size of the dataset?
Evidence: We use the publicly available dataset KVRET BIBREF5 in our experiments. This dataset is created by the Wizard-of-Oz method BIBREF10 on Amazon Mechanical Turk platform. This dataset includes dialogues in 3 domains: calendar, weather, navigation (POI) which is suitable for our mix-domain dialogue experiments. There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.
We use the publicly available dataset KVRET BIBREF5 in our experiments.
There are 2,425 dialogues for training, 302 for validation and 302 for testing, as shown in the upper half of Table TABREF12.
Answer: 3029
Question: Give me a list of 6 brands I should consider when buying new skis.
Answer: 1. K2
2. Black Crows
3. Rossignol
4. Nordica
5. Blizzard
6. Salomon
Question: Is PM Modi is honest
Answer: As far we see, PM modi Does not yet involved with any of such incident, So we can say  he is honest.
Question: What are the benefits of working out?
Answer: If you work out every day, it will help you to build a strong body and lose weight. Not only does it help you to relax your body but also reduces your anxiety and depression. Working out regularly could also help to improve your sleep quality.
Question: How do slot binary classifiers improve performance?
Evidence: This paper proposes the Flexibly-Structured Dialogue Model (FSDM) as a new end-to-end task-oriented dialogue system. The state tracking component of FSDM has the advantages of both fully structured and free-form approaches while addressing their shortcomings. On one hand, it is still structured, as it incorporates information about slots in KB schema; on the other hand, it is flexible, as it does not use information about the values contained in the KB records. This makes it easily adaptable to new values. These desirable properties are achieved by a separate decoder for each informable slot and a multi-label classifier for the requestable slots. Those components explicitly assign values to slots like the fully structured approach, while also preserving the capability of dealing with out-of-vocabulary words like the free-form approach. By using these two types of decoders, FSDM produces only valid belief states, overcoming the limitations of the free-form approach. Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details).
Further, FSDM has a new module called response slot binary classifier that adds extra supervision to generate the slots that will be present in the response more precisely before generating the final textual agent response (see Section "Methodology" for details).
Answer: by adding extra supervision to generate the slots that will be present in the response
Question: What is the function of the classifier in this paper?
Evidence: - !`Socorro, me ha picado una víbora!
- ?`Cobra?
- No, gratis.[5]
Google Translation:
- Help, I was bitten by a snake!
- Does it charge?
- Not free.
[4]https://github.com/bfarzin/haha_2019_final, Accessed on 19 June 2019 [5]https://www.fluentin3months.com/spanish-jokes/, Accessed on 19 June 2019
Humor does not translate well because it often relies on double-meaning or a subtle play on word choice, pronunciation, or context. These issues are further exacerbated in areas where space is a premium (as frequent on social media platforms), often leading to usage and development of shorthand, in-jokes, and self-reference. Thus, building a system to classify the humor of tweets is a difficult task. However, with transfer-learning and the Fast.ai library, we can build a high quality classifier in a foreign language. Our system outperforms a Naive Bayes Support Vector Machine (NBSVM) baseline, which is frequently considered a "strong baseline" for many Natural Language Processing (NLP) related tasks (see Wang et al BIBREF0 ).
Rather than hand-crafted language features, we have taken an "end to end" approach building from the raw text to a final model that achieves the tasks as presented. Our paper lays out the details of the system and our code can be found in a GitHub repository for use by other researchers to extend the state of the art in sentiment analysis.
Thus, building a system to classify the humor of tweets is a difficult task. However, with transfer-learning and the Fast.ai library, we can build a high quality classifier in a foreign language.
Answer: The classifier is used to classify the humor of tweets in a foreign language.
Question: How is MVCNN compared to CNN?
Evidence: This work presented MVCNN, a novel CNN architecture for sentence classification. It combines multichannel initialization – diverse versions of pretrained word embeddings are used – and variable-size filters – features of multigranular phrases are extracted with variable-size convolution filters. We demonstrated that multichannel initialization and variable-size filters enhance system performance on sentiment classification and subjectivity classification tasks.
This work presented MVCNN, a novel CNN architecture for sentence classification. It combines multichannel initialization – diverse versions of pretrained word embeddings are used – and variable-size filters – features of multigranular phrases are extracted with variable-size convolution filters. 
Answer: The answers are shown as follows:
* MVCNN, a novel CNN architecture for sentence classification. It combines multichannel initialization – diverse versions of pretrained word embeddings are used – and variable-size filters – features of multigranular phrases are extracted with variable-size convolution filters.
Question: Give me a list of some underrated countries I've never travelled to.
Answer: * Mongolia: This hidden gem doesn't get the attention that it needs. Known for Yak, nomad lifestyle, and Gorkhi Terelj National Park, country has a tons of things to offer.
* Ecuador: Many tourists comes to Ecuador but to visit popular Galapagos Islands. However, my doing so, they are missing all the goodness that this beautiful and filled with nature, country has to offer.
* Slovenia: If you are planning to visit the tourist beloved Croatia, then you might want to also involve its neighbouring country. Slovenia despite being small, has a magnificent Lake Bled and Triglav National Park. On the top, if you less crowded and affordable prices your wish, then it is a definite try.
* Laos: Although people are well acquainted with Thailand and Vietnam as countries in South East Asia, this small country is often overlooked. Not only this is a budget-friendly country, it has great waterfalls, caves and bunch of small islands that will definitely make worth a visit.
Question: What are all the possible answers from the magic 8 ball?
Answer: The magic 8 ball has twenty possible answers. These are divided into three general categories: positive, negative, and neutral. 

Positive: 
It is certain.
It is decidedly so.
Without a doubt.
Yes definitely.
You may rely on it.
As I see it, yes.
Most likely.
Outlook good.
Yes.
Signs point to yes.

Negative:
Don't count on it.
My reply is no.
My sources say no.
Outlook not so good.
Very doubtful.

Neutral:
Reply hazy, try again.
Ask again later.
Better not tell you now.
Cannot predict now.
Concentrate and ask again.
Question: How should I maintain my bicycle?
Answer: Keep your bicycle clean. Most important of all, keep your bicycle’s drivetrain clean. A bicycle’s drivetrain is the chain, chainrings and pulleys which make the rear wheel turn. If you can, wipe down the drivetrain with a cleaning lubricant made for bicycle drivetrains after each long ride, or every other short ride. Doing so will ensure that your ride is noticeably more enjoyable. Shifting will remain smooth and reliable, and pedaling will be easier. Dirty drivetrains are squeaky and loud. Your clean drivetrain will be nice and quiet. Your clean drivetrain will also last much longer since you are regularly cleaning away the dirt and other road grit and sweat which collects in the chain and grinds away everything it touches. If you are unable to maintain your bicycle, take it to the local bike shop at least once a year for maintenance and tune up. You won’t regret it.
Question: Who found and named Devil's Garden?
Evidence: In the early 1920s, an immigrant prospector from Hungary named Alexander Ringhoffer came across the Klondike Bluffs, a similar area with fins and arches to the west of Salt Valley, which he named Devil's Garden. Ringhoffer contacted officials at the Denver and Rio Grande Western Railroad to determine whether the land could become a tourist attraction. The railroad company realized the lucrative potential of the area and contacted the National Park Service to consider making it a national monument. President Herbert Hoover signed an executive order on April 12, 1929 that created Arches National Monument. The monument originally consisted of two parts: the Windows and Devils Garden, with the latter name being taken from Ringhoffer's name for the Klondike Bluffs, an area not initially included in the park. Arches remained a national monument until 1971 when Congress passed a bill that re-designated it as a national park.
Answer: Alexander Ringhoffer came across the area that he then named Devil's Garden.