DataHammer/scimrc huggingface dataset:
Question: What two-fold effect of multi-task training does the team conclude according to the observation?
Evidence: Figure FIGREF32 summarises the average MSD-prediction accuracy for the multi-tasking experiments discussed above. Accuracy here is generally higher than on the main task, with the multilingual finetuned setup for Spanish and the monolingual setup for French scoring best: 66.59% and 65.35%, respectively. This observation illustrates the added difficulty of generating the correct surface form even when the morphosyntactic description has been identified correctly.
We observe some correlation between these numbers and accuracy on the main task: for de, en, ru and sv, the brown, pink and blue bars here pattern in the same way as the corresponding INLINEFORM0 's in Figure FIGREF23 . One notable exception to this pattern is fr where inflection gains a lot from multilingual training, while MSD prediction suffers greatly. Notice that the magnitude of change is not always the same, however, even when the general direction matches: for ru, for example, multilingual training benefits inflection much more than in benefits MSD prediction, even though the MSD decoder is the only component that is actually shared between languages. This observation illustrates the two-fold effect of multi-task training: an auxiliary task can either inform the main task through the parameters the two tasks share, or it can help the main task learning through its regularising effect.
This observation illustrates the two-fold effect of multi-task training: an auxiliary task can either inform the main task through the parameters the two tasks share, or it can help the main task learning through its regularising effect.
Answer: An auxiliary task can either inform the main task through the parameters the two tasks share, or it can help the main task learning through its regularising effect.
Question: What evaluation metric is used?
Evidence: FLOAT SELECTED: Table 10: Results of experiments on the multi-turn adversarial task. We denote the average and one standard deviation from the results of five runs. Models that use the context as input (“with context”) perform better. Encoding this in the architecture as well (via BERT dialogue segment features) gives us the best results.
FLOAT SELECTED: Table 10: Results of experiments on the multi-turn adversarial task. We denote the average and one standard deviation from the results of five runs. Models that use the context as input (“with context”) perform better. Encoding this in the architecture as well (via BERT dialogue segment features) gives us the best results.
Answer: F1 and Weighted-F1
Question: What is the Shiba Inu
Answer: The Shiba Inu is a breed of hunting dog from Japan. A small-to-medium breed, it is the smallest of the six original and distinct spitz breeds of dog native to Japan. Its name literally translates to "brushwood dog", as it is used to flush game.
Question: What re the differences between recruiters and sourcers at a company?
Answer: In typical companies, sourcers are responsible for the upfront outreach of new, passive candidates. Sourcers leverage a variety of tools including LinkedIn, blogs, GitHub, etc. to find potential candidates and then try to contact them directly. It is the sourcers responsibility to identify as many viable candidates as possible for a role. A recruiter is responsible for managing the entire recruiting process once a candidate is identified. Recruiters will help schedule interviews, help the candidate prepare for interviews, and ultimately manage the offer process for the candidate. Recruiters are also responsible for managing the relationship with the hiring manager for a role. They are the point of contact for the entire recruiting process and are responsible for ultimately filling the role.
Question: Give me four ideas for my 7 year old daughters birthday party
Answer: Here are four ideas for a birthday party for a 7 year old girl:
- A trampoline park (make sure food is provided after!)
- A unicorn themed party with party games like pin the horn on the unicorn
- Hire an ice rink and give the party a frozen theme (Disney or otherwise!)
- Hire a community hall and do a treasure or scavenger hunt
Question: Which 3 mens tennis players have the most grand slam singles championships?
Answer: Novak Djokovic, Rafael Nadal, and Roger Federer have the most grand slam singles championships.
Question: Suggest some action movies I can watch?
Answer: Mission Impossible, Goldfinger, Speed, The Magnificent Seven, Die Hard and Top Gun are all great Action movies.
Question: Using this paragraph about Matt Eskandari, did Matt Eskandri make any films before Victim?
Evidence: Matt Eskandari attended film school at the University of Southern California. His thesis film, "The Taking," won the award for Best Student Film at Screamfest. Following film school, Eskandari was selected as a contestant for the Steven Spielberg filmmaker competition On The Lot. He would go on to make his feature debut a year later with the independent film Victim released by IFC Films, starring Stacey Haiduk, and premiered at the Cannes Film Festival. The film received a theatrical release, and was distributed on IFC Midnight's Pay-per-view channel.

In 2013 Eskandari directed and produced his film titled The Gauntlet which starred Bai Ling, and Dustin Nguyen. It debuted that October at the Screamfest film festival. Filmed in Beijing, China, the movie "follows five strangers who awake in an underground, medieval dungeon." The film received distribution from Lionsgate Home Entertainment following a screening at Cannes and was retitled Game of Assassins. For the film, Eskandari received several accolades, including the Best Director award from the Asians On Film Festival and the Winter Film Award for Best Feature Film. The official release on DVD and digital VOD was September 23, 2014.

In 2016, Eskandari began filming a new film he co-wrote entitled The Deep End. The film's cast included Tobin Bell, Nora-Jane Noone, and Alexandra Park. It was re-titled to 12 Feet Deep and released wide on June 20, 2017 by Mar Vista Entertainment.
Answer: Matt Eskandari filmed "The Taking" before his feature debut with the film Victim.
Question: What are the differences between an upright piano and a grand piano?
Answer: The main differences between an upright piano and a grand piano are:
- action mechanisms: an upright piano utilizes a string action to allow vertically placed hammers to reset, while gravity returns the hammers in a grand piano, offering better responsiveness and repetition speed
- pedals: a grand piano usually have 3 pedals, and an upright piano has 2 or 3 pedals with different functions.

Though it depends on the specific models and conditions, a grand piano usually offers better dynamic range and richer resonance than an upright piano.
Question: which of these words do not belong in the same category? peel, organic, fruit, yellow, mushy, crunchy.
Answer: Only crunchy does not belong in this category. This is because all these words describe a banana. However a banana and crunchy do not go together because a banana is mushy.
Question: What would Abraham Lincoln think of Big Data and Agile Development?
Answer: In a speech to the House in June 1848, Abraham Lincoln shared his perspective on how to execute desired change, acknowledging that charting a path to a desired outcome is not a linear path and that facts are central to the journey.

A quote that reflects Lincoln's agile mindset: "Determine that the thing can and shall be done, and then we shall find the way." In Agile development, we embrace the fact that we do not know very much about how to reach our goal. We "find the way" by communicating often, sharing facts and executing tasks that we believe will lead us to accomplishing "the thing." We focus on the goal and adapt, as needed, with the facts at hand. And regarding facts, he wanted to count all the pigs and chickens...

A quote that reflects Lincoln's thoughts on "Big Data" (collection): "He particularly objects, as I understand him, to counting all the pigs and chickens in the land. I do not perceive much force in the objection." Abe understood that decisions are advised by facts and data, yet he too warned that some data may not end up being very useful. Regarding the collection of data to guide decisions: "In that information, we shall have a stern, unbending basis of facts---a basis, in nowise subject to whim, caprice, or local interest."
Question: What's the key difficulty of the proposed method?
Evidence: In this section, we will present a cross-lingual pre-training based transfer approach. This method is designed for a common zero-shot scenario where there are a lot of source$\leftrightarrow $pivot and pivot$\leftrightarrow $target bilingual data but no source$\leftrightarrow $target parallel data, and the whole training process can be summarized as follows step by step:
Pre-train a universal encoder with source/pivot monolingual or source$\leftrightarrow $pivot bilingual data.
Train a pivot$\rightarrow $target parent model built on the pre-trained universal encoder with the available parallel data. During the training process, we freeze several layers of the pre-trained universal encoder to avoid the degeneracy issue BIBREF27.
Directly translate source sentences into target sentences with the parent model, which benefits from the availability of the universal encoder.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language invariant. In the rest of this section, we first present two existing methods yet to be explored in zero-shot translation, and then propose a straightforward but effective cross-lingual pre-training method. In the end, we present the whole training and inference protocol for transfer.
The key difficulty of this method is to ensure the intermediate representations of the universal encoder are language in variant.
Answer: To ensure the intermediate representations of the universal encoder are language in variant.
Question: What is author's opinion on why current multimodal models cannot outperform models analyzing only text?
Evidence: Despite the model trained only with images proves that they are useful for hate speech detection, the proposed multimodal models are not able to improve the detection compared to the textual models. Besides the different architectures, we have tried different training strategies, such as initializing the CNN weights with a model already trained solely with MMHS150K images or using dropout to force the multimodal models to use the visual information. Eventually, though, these models end up using almost only the text input for the prediction and producing very similar results to those of the textual models. The proposed multimodal models, such as TKM, have shown good performance in other tasks, such as VQA. Next, we analyze why they do not perform well in this task and with this data:
[noitemsep,leftmargin=*]
Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.
Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.
Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate.
Next, we analyze why they do not perform well in this task and with this data:

[noitemsep,leftmargin=*]

Noisy data. A major challenge of this task is the discrepancy between annotations due to subjective judgement. Although this affects also detection using only text, its repercussion is bigger in more complex tasks, such as detection using images or multimodal detection.

Complexity and diversity of multimodal relations. Hate speech multimodal publications employ a lot of background knowledge which makes the relations between visual and textual elements they use very complex and diverse, and therefore difficult to learn by a neural network.

Small set of multimodal examples. Fig. FIGREF5 shows some of the challenging multimodal hate examples that we aimed to detect. But although we have collected a big dataset of $150K$ tweets, the subset of multimodal hate there is still too small to learn the complex multimodal relations needed to identify multimodal hate.
Answer: The answers are shown as follows:
* Noisy data
* Complexity and diversity of multimodal relations
* Small set of multimodal examples
Question: Name the four members of the Beatles.
Evidence: The Beatles were an English rock band, formed in Liverpool in 1960, that comprised John Lennon, Paul McCartney, George Harrison and Ringo Starr. They are regarded as the most influential band of all time and were integral to the development of 1960s counterculture and popular music's recognition as an art form. Rooted in skiffle, beat and 1950s rock 'n' roll, their sound incorporated elements of classical music and traditional pop in innovative ways; the band also explored music styles ranging from folk and Indian music to psychedelia and hard rock. As pioneers in recording, songwriting and artistic presentation, the Beatles revolutionised many aspects of the music industry and were often publicised as leaders of the era's youth and sociocultural movements.
Answer: John Lennon, Paul McCartney, George Harrison and Ringo Starr.
Question: Where in the US are the most renowned Pinot Noir wines produced?
Answer: Oregon's Willamette Valley is known for producing award-winning Pinot Noir wine.
Question: What aspect of NLP research is examined?
Evidence: We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).
We focus on the following aspects of NLP research: size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender).
Answer: The answers are shown as follows:
* size, demographics, areas of research, impact, and correlation of citations with demographic attributes (age and gender)
Question: What’s the most over powered race in StarCraft 2?
Answer: Based on the mechanics of the worker units of the 3 Races in Starcraft 2; Zerg Drone, Protoss Probe, and Terran SCV, the Protoss are widely regarded as the most over powered race. Competitive Starcraft 2 requires players to gain an advantage by constantly managing their resources and building their army. The Protoss Probe has the simplest interaction to build new structures. Furthermore, the Protoss arsenal consists of some of the most expensive and powerful units in the game. The other races have means to counter the Protoss however the Community agrees the Protoss requires the least amount of skill to master.
Question: By how much is performance on CN-Celeb inferior to performance on VoxCeleb?
Evidence: FLOAT SELECTED: Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.
FLOAT SELECTED: Table 4. EER(%) results of the i-vector and x-vector systems trained on VoxCeleb and evaluated on three evaluation sets.
Answer: For i-vector system, performances are 11.75% inferior to voxceleb. For x-vector system, performances are 10.74% inferior to voxceleb
Question: What type of physicist is Arup Kumar Raychaudhuri?
Evidence: Arup Kumar Raychaudhuri (born 1 January 1952) is an Indian condensed matter physicist, materials scientist and a Distinguished Emeritus Professor at the S. N. Bose National Centre for Basic Sciences. Known for his pioneering work on the interplay of disorder and interaction, Raychaudhuri is an elected fellow of all the three major Indian science academies viz. Indian Academy of Sciences, National Academy of Sciences, India and Indian National Science Academy as well as the Asia-Pacific Academy of Materials. He is a recipient of a number of awards such as Millennium Medal of the Indian Science Congress, ICS Gold Medal of the Materials Research Society of India and FICCI Award. The Council of Scientific and Industrial Research, the apex agency of the Government of India for scientific research, awarded him the Shanti Swarup Bhatnagar Prize for Science and Technology, one of the highest Indian science awards, for his contributions to physical sciences in 1994.[note 1]
Answer: Arup Kumar Raychaudhuri is a condensed matter physicists.
Question: What are the results of this paper?
Evidence: purpleIn this paper, we propose a novel extractive summarization model especially designed for long documents, by incorporating the local context within each topic, along with the global context of the whole document.[2] purpleOur approach integrates recent findings on neural extractive summarization in a parameter lean and modular architecture.[3] purpleWe evaluate our model and compare with previous works in both extractive and abstractive summarization on two large scientific paper datasets, which contain documents that are much longer than in previously used corpora.[4] purpleOur model not only achieves state-of-the-art on these two datasets, but in an additional experiment, in which we consider documents with increasing length, it becomes more competitive for longer documents.[5] purpleWe also ran an ablation study to assess the relative contribution of the global and local components of our approach. [1] Rather surprisingly, it appears that the benefits of our model come only from modeling the local context.
For future work, we initially intend to investigate neural methods to deal with redundancy. Then, it could be beneficial to integrate explicit features, like sentence position and salience, into our neural approach. More generally, we plan to combine of traditional and neural models, as suggested by our results. Furthermore, we would like to explore more sophistical structure of documents, like discourse tree, instead of rough topic segments. As for evaluation, we would like to elicit human judgments, for instance by inviting authors to rate the outputs from different systems, when applied to their own papers. More long term, we will study how extractive/abstractive techniques can be integrated; for instance, the output of an extractive system could be fed into an abstractive one, training the two jointly.
Our model not only achieves state-of-the-art on these two datasets, but in an additional experiment, in which we consider documents with increasing length, it becomes more competitive for longer documents
Answer: Their model not only achieves state-of-the-art on these two datasets, but in an additional experiment, in which they consider documents with increasing length, it becomes more competitive for longer documents.
Question: Short Summary about 2011 Cricket World Cup
Evidence: The 2011 ICC Cricket World Cup was the tenth Cricket World Cup. It was played in India, Sri Lanka, and for the first time in Bangladesh. India won the tournament, defeating Sri Lanka by 6 wickets in the final at Wankhede Stadium in Mumbai, thus becoming the first country to win the Cricket World Cup final on home soil. India's Yuvraj Singh was declared the man of the tournament. This was the first time in World Cup history that two Asian teams had appeared in the final. It was also the first time since the 1992 World Cup that the final match did not feature Australia.

Fourteen national cricket teams took part in this tournament, including 10 full members and four associate members of the International Cricket Council (ICC). The opening ceremony was held on 17 February 2011 at Bangabandhu National Stadium, Dhaka, and the tournament was played between 19 February and 2 April. The first match was played between India and Bangladesh at the Sher-e-Bangla National Stadium in Mirpur, Dhaka.
Answer: - 2011 Cricket World Cup was played in India, Sri Lanka and Bangladesh
- India won the tournament defeating Sri Lanka.
- Yuvraj Singh was declared the Man of the Tournament
- Opening Ceremony was held on 17 February 2011 at Dhaka
Question: Is there an evolutionary root cause for self-deception?
Answer: Diverse evolutionary psychologists have conjectured about the potential advantages of self-deception in a social context, however some Terror Management theorists suggest that self-deception is actually at the core of what it means to be human. Hunter-gatherers may have used their increasing cognitive abilities to their own evolutionary benefit, but increased cognition necessarily brought along the awareness of death, which could become a major obstacle by making the dangers of day-to-day so explicit and linked to the possibility of death that would cause paralysis -thus it would not be evolutionary anymore. In conclusion, awareness of death could only have emerged in combination with self-deception, so that the terror of death would be managed and diminished. In other words, the theory dismisses the possibility of the advent of a breed of intelligent beings, unless those beings, no matter how clever they come to be, have as well the ability to conceal their own mortality.
Question: What is the billboard number one in the top 10 of 1980?
Evidence: This is a list of Billboard magazine's Top Hot 100 songs of 1980.

No.	Title	Artist(s)
1	"Call Me"	Blondie
2	"Another Brick in the Wall, Part II"	Pink Floyd
3	"Magic"	Olivia Newton-John
4	"Rock with You"	Michael Jackson
5	"Do That to Me One More Time"	Captain & Tennille
6	"Crazy Little Thing Called Love"	Queen
7	"Coming Up"	Paul McCartney
8	"Funkytown"	Lipps Inc
9	"It's Still Rock and Roll to Me"	Billy Joel
10	"The Rose"	Bette Midler
Answer: "Call Me" is a song by the American new wave band Blondie and the theme to the 1980 film American Gigolo. "Call Me" was No. 1 for six consecutive weeks on the Billboard Hot 100 chart, where it became the band's biggest single and second No. 1.
Question: My refrigerator broke for six days while I was on vacation. Which of the following foods would be still safe to eat unrefrigerated for that long: raw chicken, a jar of olives, hard cheese, milk, and fruit jelly.
Answer: Olives, hard cheese, and fruit jelly are not likely to spoil unrefrigerated for a couple of days. However, raw chicken and milk would be unsafe to eat.
Question: What is the author's research direction for his future work?
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.
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.
Answer: 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.