DataHammer/scimrc huggingface dataset:
Question: Right data distribution strategy for Azure Synapse
Answer: A round-robin table to create and delivers fast performance tables when used as a staging table for loads.
Hashing by distribution column then dictates how the data is spread across the underlying distributions
Replication is very good for small lookup tables or dimension tables that are frequently joined with other big tables.
Question: How is SLC task evaluated?
Evidence: FLC is a composition of two subtasks: the identification of the propagandist text fragments and the identification of the techniques used (18-way classification task). While F$_1$ measure is appropriate for a multi-class classification task, we modified it to account for partial matching between the spans; see BIBREF10 for more details. We further computed an F$_1$ value for each propaganda technique (not shown below for the sake of saving space, but available on the leaderboard).
SLC is a binary classification task with imbalanced data. Therefore, the official evaluation measure for the task is the standard F$_1$ measure. We further report Precision and Recall.
The results on the test set for the SLC task are shown in Table TABREF33, while Table TABREF34 presents the results on the development set at the end of phase 1 (cf. Section SECREF6). The general decrease of the F$_1$ values between the development and the test set could indicate that systems tend to overfit on the development set. Indeed, the winning team ltuorp chose the parameters of their system both on the development set and on a subset of the training set in order to improve the robustness of their system.
Tables TABREF36 and TABREF41 report the results on the test and on the development sets for the FLC task. For this task, the results tend to be more stable across the two sets. Indeed, team newspeak managed to almost keep the same difference in performance with respect to team Antiganda. Note that team MIC-CIS managed to reach the third position despite never having submitted a run on the development set.
SLC is a binary classification task with imbalanced data. Therefore, the official evaluation measure for the task is the standard F1 measure. We further report Precision and Recall.
Answer: The official evaluation measure for the task is the standard F1 measure. They further report Precision and Recall.
Question: What were their accuracy results on the task?
Evidence: FLOAT SELECTED: Table 3: Lemmatization accuracy using WikiNews testset
FLOAT SELECTED: Table 3: Lemmatization accuracy using WikiNews testset
Answer: 97.32%
Question: What is the name of the Winslow's nerdy next-door neighbor on the TV show "Family Matters"?
Answer: Steve Urkel
Question: What are the main ingredients of deviled eggs and what are some unique add-in ideas?
Answer: Traditional deviled eggs are made of boiled eggs with the yolk mixed with mayo and light seasoning. Some unique variations to try could include sriracha, avocado, bacon, jalapeno, relish, smoked salmon, curry, horseradish, dill, or feta cheese.
Question: What are Nanotube?
Answer: Nanotubes, often known as Carbon Nanotubes, are light, strong shapes made of Carbon atoms.  Nanotubes are shaped like tiny straws with walls as thin as one carbon atom.  They are considered as the strongest of any material known to man.  Nanotubes have many practical uses, one of which is to make the darkest black paint.
Question: Do they use supervised learning in heuristics of similarity measuring?
Evidence: In this part, we introduce several heuristics of similarity measuring based on word embeddings. Notice that, we do not leverage supervised learning (e.g., full neural networks for sentence paring BIBREF27 , BIBREF28 ) to measure similarity, because it is costly to obtain labeled data of high quality.
The simplest approach, perhaps, is to sum over all word embeddings in an utterance as sentence-level features INLINEFORM0 . This heuristic is essentially the sum pooling method widely used in neural networks BIBREF29 , BIBREF30 , BIBREF27 . The cosine measure is used as the similarity score between two utterances INLINEFORM1 and INLINEFORM2 . Let INLINEFORM3 and INLINEFORM4 be their sentence vectors; then we have DISPLAYFORM0 
where INLINEFORM0 is the INLINEFORM1 -norm of a vector.
To enhance the interaction between two successive sentences, we propose a more complicated heuristic as follows. Let INLINEFORM0 and INLINEFORM1 be a word in INLINEFORM2 and INLINEFORM3 , respectively. (Embeddings are denoted as bold alphabets.) Suppose further that INLINEFORM4 and INLINEFORM5 are the numbers of words in INLINEFORM6 and INLINEFORM7 . The similarity is given by DISPLAYFORM0 
For each word INLINEFORM0 in INLINEFORM1 , our intuition is to find the most related word in INLINEFORM2 , given by the INLINEFORM3 part; their relatedness is also defined by the cosine measure. Then the sentence-level similarity is obtained by the average similarity score of words in INLINEFORM4 . This method is denoted as heuristic-max.
Alternatively, we may substitute the INLINEFORM0 operator in Equation ( EQREF16 ) with INLINEFORM1 , resulting in the heuristic-avg variant, which is equivalent to the average of word-by-word cosine similarity. However, as shown in Subsection SECREF22 , intensive similarity averaging has a “blurring” effect and will lead to significant performance degradation. This also shows that our proposed heuristic-max does capture useful interaction between two successive utterances in a dialogue.
In this part, we introduce several heuristics of similarity measuring based on word embeddings. Notice that, we do not leverage supervised learning (e.g., full neural networks for sentence paring [28, 29]) to measure similarity, because it is costly to obtain labeled data of high quality.
Answer: No.
Question: Identify which car manufacturer is Italian or American: Maserati, RAM
Answer: RAM is American, Maserati is Italian
Question: How should I sleep train my baby?
Answer: Make sure the room is dark enough. If you hold out your hand at arm's length, you should not be able to see the outline of your fingers. Get a sound machine so that outside noises do not disturb them. Use a sleep sack or a swaddle as a safe method of keeping the baby warm. Lastly, maintain a consistent routine every night which may include reading books, giving a bath, feeding them milk, and/or singing a song.
Question: What are some movies about Artificial Intelligence and Machine Consciousness?
Answer: Here are some Hollywood movies that explore the concept of Artificial Intelligence and Machine Consciousness - 

* Ex Machina
* The Matrix
* Blade Runner
* The Terminator
* Wall-E
* Her
* I, Robot
* 2001: A Space Odyssey
Question: Please list all the countries having hosted a Rugby World Cup before 2023 according to the text
Evidence: Tournaments are organised by Rugby World Cup Ltd (RWCL), which is itself owned by World Rugby. The selection of host is decided by a vote of World Rugby Council members.The voting procedure is managed by a team of independent auditors, and the voting kept secret. The host nation is generally selected five or six years before the competition.
The tournament has been hosted by multiple nations. For example, the 1987 tournament was co-hosted by Australia and New Zealand. World Rugby requires that the hosts must have a venue with a capacity of at least 60,000 spectators for the final. Host nations sometimes construct or upgrade stadia in preparation for the World Cup, such as Millennium Stadium – purpose built for the 1999 tournament – and Eden Park, upgraded for 2011. The first country outside of the traditional rugby nations of SANZAAR or the Six Nations to be awarded the hosting rights was 2019 host Japan. France will host the 2023 tournament. The next tournament to be hosted by a nation outside the traditional nations will be the 2031 tournament in the United States.
Answer: Australia, New Zealand, Japan
Question: What features do they experiment with?
Evidence: All models are trained with CTC-loss. Input features are 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, concatenated with deltas and delta-deltas (120 features in vector). We also tried to use spectrogram and experimented with different normalization techniques.
Input features are 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window, concatenated with deltas and delta-deltas (120 features in vector). We also tried to use spectrogram and experimented with different normalization techniques.
Answer: The answers are shown as follows:
* 40 mel-scaled log filterbank enegries (FBanks) computed every 10 ms with 25 ms window
* deltas and delta-deltas (120 features in vector)
* spectrogram
Question: What are the parts of a sail?
Answer: The parts of a sail are: Head, Leech, Clew, Foot, Tack, and Luff.
Question: How best should I travel from London to Edinburgh, UK ?
Answer: There are 3 main options for traveling between London and Edinburgh, UK. By car you will take the A1 north from London almost 7-8 hours depending on traffic. By train there is now an express train getting you from London Kings Cross to Edinburgh in under 4 hours. By plane you can fly from London City, London Gatwick or London Heathrow. The flight itself is only about 30-45mins, but with checkin and travel to either Heathrow or Gatwick the total travel time would also be close to 3-4 hours.
Question: Does the NER pretraining using the extra corpus, soft label embedding, and auxiliary sentence-level relation classification prediction improve the F1 score?
Evidence: Results on SKE dataset are presented in Table 1. The baseline model is based on the Google BERT, use hard label embedding and train on only SKE dataset without NER pretraining. As shown in table 1, the F1 score increase from 0.864 to 0.871 when combined with our enhanced BERT. NER pretraining using the extra corpus, soft label embedding and auxiliary sentence-level relation classification prediction also improve the F1 score. Combined all of these contributions, we achieve F1-score 0.876 with the single model on test set 1.
NER pretraining using the extra corpus, soft label embedding and auxiliary sentence-level relation classification prediction also improve the F1 score.
Answer: Yes, they do.
Question: Tell me whether each of these things smells good or bad: candle, pig, skunk, poop, febreze, incense
Answer: Candle: Smells Good
Pig: Smells Bad
Skunk: Smells Bad
Poop: Smells Bad
Febreze: Smells Good
Incense: Smells Good
Question: What is a Cigar
Evidence: A cigar is a rolled bundle of dried and fermented tobacco leaves made to be smoked. Cigars are produced in a variety of sizes and shapes. Since the 20th century, almost all cigars are made of three distinct components: the filler, the binder leaf which holds the filler together, and a wrapper leaf, which is often the highest quality leaf used. Often there will be a cigar band printed with the cigar manufacturer's logo. Modern cigars often come with two bands, especially Cuban cigar bands, showing Limited Edition (Edición Limitada) bands displaying the year of production.
Answer: A cigar is a rolled bundle of dried and fermented tobacco leaves made to be smoked. Cigars are produced in a variety of sizes and shapes. Since the 20th century, almost all cigars are made of three distinct components: the filler, the binder leaf which holds the filler together, and a wrapper leaf, which is often the highest quality leaf used.
Question: Why do people like sports?
Answer: Many people like to play and watch sports in their free time. Sports a great way to stay active and get exercise. When you take part in a team sport, there is sense of camaraderie as you and your teammates achieve a shared goal. It is also fun to watch sports, since many of these athletes are the best in their craft. In addition, since many cities have their own teams, there is a sense of community when you and your neighbors can support a team together.
Question: Which award did Rustam win and in what year was it awarded?
Evidence: Rustam Gasparyan was an Armenian military officer and politician. In 2020 he was posthumously awarded the Hero of Artsakh military award.
Answer: The Hero of Artsakh military award, in 2020
Question: What are the three original citrus fruits?
Answer: Contrary to popular belief, the three original citrus fruits are the mandarin, pomelo and citron. All other popular fruits such as the bitter orange, lemons or grapefruit are all hybrids.
Question: What principles does conversion rate optimization share with direct response marketing?
Evidence: Online conversion rate optimization (or website optimization) was born out of the need of e-commerce marketers to improve their website's performance in the aftermath of the dot-com bubble, when technology companies started to be more aware about their spending, investing more in website analytics. After the burst, with website creation being more accessible, tons of pages with bad user experience were created. As competition grew on the web during the early 2000s, website analysis tools became available, and awareness of website usability grew, internet marketers were prompted to produce measurables for their tactics and improve their website's user experience.

In 2004, new tools enabled internet marketers to experiment with website design and content variations to determine which layouts, copy text, offers, and images perform best. Testing started to be more accessible and known. This form of optimization accelerated in 2007 with the introduction of the free tool Google Website Optimizer. Today, optimization and conversion are key aspects of many digital marketing campaigns. A research study conducted among internet marketers in 2017, for example, showed that 50% of respondents thought that CRO was "crucial to their overall digital marketing strategy".

Conversion rate optimization shares many principles with direct response marketing – a marketing approach that emphasizes tracking, testing, and on-going improvement. Direct marketing was popularized in the early twentieth century and supported by the formation of industry groups such as the Direct Marketing Association, which was formed in 1917 and later named Data & Marketing Association and acquired by the Association of National Advertisers following the announcement on May 31, 2018.

Like modern day conversion rate optimization, direct response marketers also practice A/B split-testing, response tracking, and audience testing to optimize mail, radio, and print campaigns.
Answer: They are both marketing approaches that emphasize tracking, testing, and on-going improvement. Direct response marketers also practice A/B split-testing, response tracking, and audience testing to optimize mail, radio, and print campaigns.
Question: Identify which instrument is string or percussion: Wood block, Akkordolia
Answer: Akkordolia is string, Wood block is percussion.
Question: How are rocks formed?
Answer: Rocks are formed from molten rock from volcanos.  When the lava cools new earth is formed after it solidifies.  Sediment rocks are forms when particles in the air settle because of rain, or when minerals from water gather and cause mineral scale.  You also get rocks when layers of earth (e.g. dirt) build on top of each other and compress the layers beneath it for millions of years.
Question: Why the WIKIQA shows a significantly lower accuracy of 12.47%?
Evidence: This section describes another selection-based QA task, called answer retrieval, that finds the answer context from a larger dataset, the entire Wikipedia. SQuAD provides no mapping of the answer contexts to Wikipedia, whereas WikiQA and SelQA provide mappings; however, their data do not come from the same version of Wikipedia. We propose an automatic way of mapping the answer contexts from all corpora to the same version of Wikipeda so they can be coherently used for answer retrieval.
Each paragraph in Wikipedia is first indexed by Lucene using {1,2,3}-grams, where the paragraphs are separated by WikiExtractor and segmented by NLP4J (28.7M+ paragraphs are indexed). Each answer sentence from the corpora in Table TABREF3 is then queried to Lucene, and the top-5 ranked paragraphs are retrieved. The cosine similarity between each sentence in these paragraphs and the answer sentence is measured for INLINEFORM0 -grams, say INLINEFORM1 . A weight is assigned to each INLINEFORM2 -gram score, say INLINEFORM3 , and the weighted sum is measured: INLINEFORM4 . The fixed weights of INLINEFORM5 are used for our experiments, which can be improved.
If there exists a sentence whose INLINEFORM0 , the paragraph consisting of that sentence is considered the silver-standard answer passage. Table TABREF3 shows how robust these silver-standard passages are based on human judgement ( INLINEFORM1 ) and how many passages are collected ( INLINEFORM2 ) for INLINEFORM3 , where the human judgement is performed on 50 random samples for each case. For answer retrieval, a dataset is created by INLINEFORM4 , which gives INLINEFORM5 accuracy and INLINEFORM6 coverage, respectively. Finally, each question is queried to Lucene and the top- INLINEFORM7 paragraphs are retrieved from the entire Wikipedia. If the answer sentence exists within those retrieved paragraphs according to the silver-standard, it is considered correct.
Finding a paragraph that includes the answer context out of the entire Wikipedia is an extremely difficult task (128.7M). The last row of Table TABREF3 shows results from answer retrieval. Given INLINEFORM0 , SelQA and SQuAD show about 34% and 35% accuracy, which are reasonable. However, WikiQA shows a significantly lower accuracy of 12.47%; this is because the questions in WikiQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search.
However, WIKIQA shows a significantly lower accuracy of 12.47%; this is because the questions in WIKIQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search.
Answer: This is because the questions in WIKIQA is about twice shorter than the questions in the other corpora such that not enough lexicons can be extracted from these questions for the Lucene search.
Question: Given a reference text about the Kaziranga Elephant Festival, tell me who organizes it.
Evidence: The Kaziranga Elephant Festival is a yearly elephant festival held in the Kaziranga National Park of Assam for the conservation and protection of Asiatic elephant. The festival is jointly organised by the Forest Department and Tourism Department of Assam with an objective to highlight and find ways to resolve the increasing man-elephant conflict. Hundreds of domestic Asiatic elephants, decorated from head to toe, participate in the program. They take part in parade, races, football and dance earning praises from the spectators.
Answer: The Kaziranga Elephant Festival is organized by the Forest and Tourism Departments of Assam.
Question: What existing methods is SC-GPT compared to?
Evidence: We compare with three baseline methods. $({1})$ SC-LSTM BIBREF3 is a canonical model and a strong baseline that uses an additional dialog act vector and a reading gate to guide the utterance generation. $({2})$ GPT-2 BIBREF6 is used to directly fine-tune on the domain-specific labels, without pre-training on the large-scale corpus of (dialog act, response) pairs. $({3})$ HDSA BIBREF7 is a state-of-the-art model on MultiWOZ. It leverages dialog act structures to enable transfer in the multi-domain setting, showing superior performance than SC-LSTM.
We compare with three baseline methods. $({1})$ SC-LSTM BIBREF3 is a canonical model and a strong baseline that uses an additional dialog act vector and a reading gate to guide the utterance generation. $({2})$ GPT-2 BIBREF6 is used to directly fine-tune on the domain-specific labels, without pre-training on the large-scale corpus of (dialog act, response) pairs. $({3})$ HDSA BIBREF7 is a state-of-the-art model on MultiWOZ. It leverages dialog act structures to enable transfer in the multi-domain setting, showing superior performance than SC-LSTM.
Answer: The answers are shown as follows:
* $({1})$ SC-LSTM BIBREF3
* $({2})$ GPT-2 BIBREF6 
* $({3})$ HDSA BIBREF7
Question: What defects do the existing ASR systems have?
Evidence: Automatic Speech Recognition (ASR) systems have been evolving quickly and reaching human parity in certain cases. The systems usually perform pretty well on reading style and clean speech, however, most of the available systems suffer from situation where the speaking style is conversation and in noisy environments. It is not straight-forward to tackle such problems due to difficulties in data collection for both speech and text. In this paper, we attempt to mitigate the problems using language models combination techniques that allows us to utilize both large amount of writing style text and small number of conversation text data. Evaluation on the VLSP 2019 ASR challenges showed that our system achieved 4.85% WER on the VLSP 2018 and 15.09% WER on the VLSP 2019 data sets.
Automatic Speech Recognition (ASR) systems have been evolving quickly and reaching human parity in certain cases. The systems usually perform pretty well on reading style and clean speech, however, most of the available systems suffer from situation where the speaking style is conversation and in noisy environments.
Answer: Most of the available systems suffer from situation where the speaking style is conversation and in noisy environments.