DataHammer/scimrc huggingface dataset:
Question: How do they correlate NED with emotional bond levels?
Evidence: According to prior work, both from domain theory BIBREF16 and from experimental validation BIBREF6 , a high emotional bond in patient-therapist interactions in the suicide therapy domain is associated with more entrainment. In this experiment, we compute the correlation of the proposed NED measure with the patient-perceived emotional bond ratings. Since the proposed measure is asymmetric in nature, we compute the measures for both patient-to-therapist and therapist-to-patient entrainment. We also compute the correlation of emotional bond with the baselines used in Experiment 1. We report Pearson's correlation coefficients ( INLINEFORM0 ) for this experiment in Table TABREF26 along with their INLINEFORM1 -values. We test against the null hypothesis INLINEFORM2 that there is no linear association between emotional bond and the candidate measure.
In this experiment, we compute the correlation of the proposed NED measure with the patient-perceived emotional bond ratings. Since the proposed measure is asymmetric in nature, we compute the measures for both patient-to-therapist and therapist-to-patient entrainment.
We report Pearson's correlation coefficients ( INLINEFORM0 ) for this experiment in Table TABREF26 along with their INLINEFORM1 -values. 
Answer: They compute Pearson’s correlation between NED measure for patient-to-therapist and patient-perceived emotional bond rating and NED measure for therapist-to-patient and patient-perceived emotional bond rating
Question: Which of the following items are for little kids? 
toy, car, airplane, diaper, sippy cup, computer, lighter, onesie, and microwave.
Answer: Toy, diaper, sippy cup, and onesie.
Question: When being in cold weather, categorize each of the following as "Useful" or "Not Useful": Coat, Air Condition, Ice, Beanie, Pants, Bikini
Answer: Coat: Useful
Air Condition: Not Useful
Ice: Not Useful
Beanie: Useful
Pants: Useful
Bikini: Not Useful
Question: What task-specific features are used?
Evidence: In total, we define 26 features for describing each token: 1)POS tag; 2)Chunk tag; 3)Position of the token within the text, normalized between 0 and 1; 4)If the token starts with a capital letter; 5)If the token is a digit. Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types ("soprano", "violinist", etc.); 9)Classical work types ("symphony", "overture", etc.); 10)Musical instruments; 11)Opus forms ("op", "opus"); 12)Work number forms ("no", "number"); 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp"); 14)Work Modes ("major", "minor", "m"). Finally, we complete the tokens' description including as token's features the surface form, the POS and the chunk tag of the previous and the following two tokens (12 features).
Gazetteers: 6)Contributor first names; 7)Contributor last names; 8)Contributor types ("soprano", "violinist", etc.); 9)Classical work types ("symphony", "overture", etc.); 10)Musical instruments; 11)Opus forms ("op", "opus"); 12)Work number forms ("no", "number"); 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp"); 14)Work Modes ("major", "minor", "m").
Answer: The answers are shown as follows:
* 6)Contributor first names
* 7)Contributor last names
* 8)Contributor types ("soprano", "violinist", etc.)
* 9)Classical work types ("symphony", "overture", etc.)
* 10)Musical instruments
* 11)Opus forms ("op", "opus")
* 12)Work number forms ("no", "number")
* 13)Work keys ("C", "D", "E", "F" , "G" , "A", "B", "flat", "sharp")
* 14)Work Modes ("major", "minor", "m")
Question: Based on the passage below, what do scientologists believe cause neuroses and mental problems?
Evidence: Scientology texts say that a human possesses an immortal inner self, termed a thetan, that resides in the physical body and has experienced many past lives. Scientologists believe that traumatic events experienced by the thetan over its lifetimes have resulted in negative "engrams" forming in the mind, causing neuroses and mental problems. They claim that the practice of auditing can remove these engrams; Scientology groups charge fees for clients undergoing auditing. Once an auditing practitioner deems the engrams to have been removed, an individual is given the status of "clear". They can take part in a further series of activities that are termed "Operating Thetan" (OT) levels, which require further payments.
Answer: Scientologists believe that neuroses and mental problems are caused by traumatic events experienced in past lives. These traumatic experiences result in negative "engrams" forming in the mind.
Question: Does the corpus contain only formal language?
Evidence: End-to-end speech-to-text translation (ST) has attracted much attention recently BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 given its simplicity against cascading automatic speech recognition (ASR) and machine translation (MT) systems. The lack of labeled data, however, has become a major blocker for bridging the performance gaps between end-to-end models and cascading systems. Several corpora have been developed in recent years. post2013improved introduced a 38-hour Spanish-English ST corpus by augmenting the transcripts of the Fisher and Callhome corpora with English translations. di-gangi-etal-2019-must created the largest ST corpus to date from TED talks but the language pairs involved are out of English only. beilharz2019librivoxdeen created a 110-hour German-English ST corpus from LibriVox audiobooks. godard-etal-2018-low created a Moboshi-French ST corpus as part of a rare language documentation effort. woldeyohannis provided an Amharic-English ST corpus in the tourism domain. boito2019mass created a multilingual ST corpus involving 8 languages from a multilingual speech corpus based on Bible readings BIBREF7. Previous work either involves language pairs out of English, very specific domains, very low resource languages or a limited set of language pairs. This limits the scope of study, including the latest explorations on end-to-end multilingual ST BIBREF8, BIBREF9. Our work is mostly similar and concurrent to iranzosnchez2019europarlst who created a multilingual ST corpus from the European Parliament proceedings. The corpus we introduce has larger speech durations and more translation tokens. It is diversified with multiple speakers per transcript/translation. Finally, we provide additional out-of-domain test sets.
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice BIBREF10 for 11 languages into English, diversified with over 11,000 speakers and over 60 accents. It includes a total 708 hours of French (Fr), German (De), Dutch (Nl), Russian (Ru), Spanish (Es), Italian (It), Turkish (Tr), Persian (Fa), Swedish (Sv), Mongolian (Mn) and Chinese (Zh) speeches, with French and German ones having the largest durations among existing public corpora. We also collect an additional evaluation corpus from Tatoeba for French, German, Dutch, Russian and Spanish, resulting in a total of 9.3 hours of speech. Both corpora are created at the sentence level and do not require additional alignments or segmentation. Using the official Common Voice train-development-test split, we also provide baseline models, including, to our knowledge, the first end-to-end many-to-one multilingual ST models. CoVoST is released under CC0 license and free to use. The Tatoeba evaluation samples are also available under friendly CC licenses. All the data can be acquired at https://github.com/facebookresearch/covost.
In this paper, we introduce CoVoST, a multilingual ST corpus based on Common Voice (Ardila et al., 2019) for 11 languages into English, diversified with over 11,000 speakers and over 60 accents.
Answer: No,it covers over 60 accents.
Question: Why are pools fun?
Answer: Pools are fun because they help to keep people cool when it is hot outside.  Kids love jumping in pool and playing with other kids in the pool too.  Sometimes water slides are connected to pools to offer more fun.  Pools are a great way to relax in the sun, get a tan, or socialize with your friends.  They do have a fair amount of upkeep during the very hot and very cold seasons of the year.  Pools can freeze in the winter and also develop algae in the hot humid weather too.  Chlorine is used to disinfect pools and neutralize in viruses and bacteria.
Question: Based on the passage below, summarize Beethoven's contributions to western music.
Evidence: Beethoven expanded the formal and emotional scope – not to mention length – of nearly every genre in which he wrote. While he is most famous for his heightening of the symphonic form, Beethoven also had a dramatic influence on the piano sonata, violin sonata, string quartet and piano concerto, among several others. Only in the realm of vocal composition – opera and the mass – was his effect on later generations muted.

Beethoven's stylistic innovations bridge the Classical and Romantic periods. The works of his early period brought the Classical form to its highest expressive level, expanding in formal, structural, and harmonic terms the musical idiom developed by predecessors such as Mozart and Haydn. The works of his middle period were more forward-looking, contributing to the musical language and thinking of the Romantic era, inspiring composers such as Franz Schubert, Felix Mendelssohn, Robert Schumann, Franz Liszt, Richard Wagner, and Johannes Brahms. His late period works were characterized by formal, harmonic, and structural experimentation at the highest level, often pointing toward contrapuntal tendencies and microscopic textures, as well as an increasingly introverted compositional outlook. Though rightly credited as a major harbinger of the Romantic era in music that followed, Beethoven never abandoned the fundamental formal paradigms and generally objective artistic philosophy characterizing musical Classicism to the same extent that later composers such as Berlioz or even Schubert did.
Answer: - Heightened the symphonic form and had a dramatic influence on the piano sonata, violin sonata, string quartet, piano concertos and other genres
- Contributed to the musical language and thinking of the romantic era
- Formal, harmonic and structural experimentation in his later works
Question: Given a reference text about Sam McElroy, at what school was he the head football coach and what was his overall record there?
Evidence: Sam McElroy (born c. 1963) is an American football coach. He served as head football coach at Tarleton State University from 2005 to 2009, compiling a record of 40–15.

McElroy is a 1985 graduate of Southwest Texas State University (now Texas State University), where he did not play varsity football but served as student assistant to head coach John O'Hara. Soon after graduation he became assistant coach at Corsicana High School. In 1989, he was hired by Grant Teaff to serve as graduate assistant at Baylor University. He later had short stints at Navarro College and at Alto High School, where he was named 1992 District 22-AA Coach of the Year after winning the district title and earning a state playoff berth.
Answer: Sam McElroy was the head football coach of Tarleton State University and had a combined record of 40-15.
Question: How is the semi-structured knowledge base created?
Evidence: Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques. First, the table schemas were manually defined based on the syllabus, study guides, and training questions. Tables were then populated both manually and semi-automatically using IKE BIBREF29 , a table-building tool that performs interactive, bootstrapped relation extraction over a corpus of science text. In addition, to augment these tables with the broad knowledge present in study guides that doesn't always fit the manually defined table schemas, we ran an Open IE BIBREF30 pattern-based subject-verb-object (SVO) extractor from BIBREF31 clark2014:akbc over several science texts to populate three-column Open IE tables. Methods for further automating table construction are under development.
Although techniques for constructing this knowledge base are outside the scope of this paper, we briefly mention them. Tables were constructed using a mixture of manual and semi-automatic techniques
Answer: The answers are shown as follows:
* using a mixture of manual and semi-automatic techniques
Question: given the following text, tell me the most popular method for training multi-layer perceptrons today and explain how it works
Evidence: Today, the most popular method for training multi-layer perceptrons (MLPs) is back-propagation. The terminology "back-propagating errors" was introduced in 1962 by Frank Rosenblatt, but he did not know how to implement this, although Henry J. Kelley had a continuous precursor of backpropagation already in 1960 in the context of control theory. Modern backpropagation is actually Seppo Linnainmaa's general reverse mode of automatic differentiation (1970) for discrete connected networks of nested differentiable functions. It is an efficient application of the chain rule (derived by Gottfried Wilhelm Leibniz in 1673) to networks of differentiable nodes. In 1982, Paul Werbos applied backpropagation to MLPs in the way that has become standard. In 1985, David E. Rumelhart et al. published an experimental analysis of the technique. Many improvements have been implemented in subsequent decades.

During backpropagation, the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by some small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to some state where the error of the calculations is small. In this case, one would say that the network has learned a certain target function. To adjust weights properly, one applies a general method for non-linear optimization that is called gradient descent, due to Augustin-Louis Cauchy, who first suggested it in 1847. For this, the network calculates the derivative of the error function with respect to the network weights, and changes the weights such that the error decreases (thus going downhill on the surface of the error function). For this reason, back-propagation can only be applied on networks with differentiable activation functions.
Answer: Today, the most popular method for training multi-layer perceptrons (MLPs) is back-propagation. During backpropagation, the output values are compared with the correct answer to compute the value of some predefined error-function. The error is then fed back through the network. Using this information, the algorithm adjusts the weights of each connection in order to reduce the value of the error function by a small amount. After repeating this process for a sufficiently large number of training cycles, the network will usually converge to a state where the error of the calculations is small. In this case, one would say that the network has learned a certain target function
Question: How much in experiments is performance improved for models trained with generated adversarial examples?
Evidence: Adversarial training can often improve model robustness BIBREF25, BIBREF27. We also fine-tune the target models using adversarial training. At each training step, we train the model with a batch of original examples along with adversarial examples with balanced labels. The adversarial examples account for around 10% in a batch. During training, we generate adversarial examples with the current model as the target and update the model parameters with the hybrid batch iteratively. The beam size for generation is set to 1 to reduce the computation cost, since the generation success rate is minor in adversarial training. We evaluate the adversarially trained models, as shown in Table TABREF18.
After adversarial training, the performance of all the target models raises significantly, while that on the original examples remain comparable. Note that since the focus of this paper is on model robustness which can hardly be reflected in original data, we do not expect performance improvement on original data. The results demonstrate that adversarial training with our adversarial examples can significantly improve the robustness we focus on without remarkably hurting the performance on original data. Moreover, although the adversarial example generation is constrained by a BERT language model, BiMPM and DIIN which do not use the BERT language model can also significantly benefit from the adversarial examples, further demonstrating the effectiveness of our method.
We evaluate the adversarially trained models, as shown in Table TABREF18.

After adversarial training, the performance of all the target models raises significantly, while that on the original examples remain comparable.
Answer: Answer with content missing: (Table 1) The performance of all the target models raises significantly, while that on the original
examples remain comparable (e.g. the overall accuracy of BERT on modified examples raises from 24.1% to 66.0% on Quora)
Question: Who were the commanders of the two armies involved in the Battle of Hastings?
Answer: The Norman-French army was commanded by William, the Duke of Normandy. The English army fought for King Harold Godwinson.
Question: What are the components of a passive crossover?
Evidence: A passive crossover is an electronic circuit that uses a combination of one or more resistors, inductors and capacitors. These components are combined to form a filter network and are most often placed between the full frequency-range power amplifier and the loudspeaker drivers to divide the amplifier's signal into the necessary frequency bands before being delivered to the individual drivers. Passive crossover circuits need no external power beyond the audio signal itself, but have some disadvantages: they may require larger inductors and capacitors due to power handling requirements. Unlike active crossovers which include a built-in amplifier, passive crossovers have an inherent attenuation within the passband, typically leading to a reduction in damping factor before the voice coil
Answer: A passive crossover is a loudspeaker circuit comprised of resistors, inductors, and capacitors.
Question: What are some questions I can ask my mom to get to know her better?
Answer: • What were your first few years of motherhood like for you?
• Was there any family drama you protected me from when I was younger?
• What is your happiest memory of us?
• What do you look forward to?
• What was the most memorable thing I ever did for you?
• What's something you want me to always remember after you're gone?
• What's your favorite thing to do with me, or with our family?
Question: Who is Teresa Giudice?
Evidence: Teresa Giudice (/ˈdʒuːdɪtʃeɪ/ JOO-ditch-ay, Italian: [teˈrɛːza ˈdʒuːditʃe]; née Gorga; born May 18, 1972) is an American television personality best known for starring in The Real Housewives of New Jersey. Besides appearing on the show, Giudice wrote multiple New York Times bestseller cookbooks and was featured on Donald Trump's The Celebrity Apprentice 5 (2012).
Answer: Teresa Giudice is a reality TV star who is most famous for Bravo's The Real Housewives of New Jersey. She is also a New York Times best selling cookbook author who specializes in Italian cuisine.
Question: Extract the sorting algorithms mentioned in the following paragraph.
Evidence: While there are a large number of sorting algorithms, in practical implementations a few algorithms predominate. Insertion sort is widely used for small data sets, while for large data sets an asymptotically efficient sort is used, primarily heapsort, merge sort, or quicksort. Efficient implementations generally use a hybrid algorithm, combining an asymptotically efficient algorithm for the overall sort with insertion sort for small lists at the bottom of a recursion. Highly tuned implementations use more sophisticated variants, such as Timsort (merge sort, insertion sort, and additional logic), used in Android, Java, and Python, and introsort (quicksort and heapsort), used (in variant forms) in some C++ sort implementations and in .NET.

For more restricted data, such as numbers in a fixed interval, distribution sorts such as counting sort or radix sort are widely used. Bubble sort and variants are rarely used in practice, but are commonly found in teaching and theoretical discussions.

When physically sorting objects (such as alphabetizing papers, tests or books) people intuitively generally use insertion sorts for small sets. For larger sets, people often first bucket, such as by initial letter, and multiple bucketing allows practical sorting of very large sets. Often space is relatively cheap, such as by spreading objects out on the floor or over a large area, but operations are expensive, particularly moving an object a large distance – locality of reference is important. Merge sorts are also practical for physical objects, particularly as two hands can be used, one for each list to merge, while other algorithms, such as heapsort or quicksort, are poorly suited for human use. Other algorithms, such as library sort, a variant of insertion sort that leaves spaces, are also practical for physical use.
Answer: Insertion sort, heap sort, merge sort, quick sort, timsort, radix sort, bubble sort, bucket sort, library sort, and hybrid algorithms combining multiple sort algorithms.
Question: What is the performance of their model?
Evidence: We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction. The predictions confusion matrix is shown in table TABREF46 . The DDIs other than false being classified as false makes most of the classification error. It may perform better if a classifier which can tells true and false DDI apart is trained. We leave this two-stage classifier to our future work. Another phenomenon is that the “Int” type is often classified as “Effect”. The “Int” sentence describes there exists interaction between two drugs and this information implies the two drugs' combination will have good or bed effect. That's the reason why “Int” and “Effect” are often obfuscated.
We compare our best F1 score with other state-of-the-art approaches in table TABREF39 , which shows our model has competitive advantage in dealing with drug-drug interaction extraction.
Answer: Answer with content missing: (Table II) Proposed model has F1 score of  0.7220.