DataHammer/scimrc huggingface dataset:
Question: What result of [Gulrajani & Lopez-Paz, 2020] do we dispute?
Evidence: At the start of this section we set out to address three questions, which can be answered as: (1) The dependence of cross-domain generalisation on complexity can be directly and precisely when using linear models.
(2) The erratic performance of state of the art methods in DomainBed can be largely explained in terms of implied model complexity.
(3) If the goal is to optimise for domain generalisation, then then domain-wise validation is preferred to instance-wise validation as a modelselection objective.
Our analysis suggests that several existing methods that tried to tune learnable DG hyper-parameters by performance on a held-out domain were broadly on the right track, and concurs with Gulrajani & Lopez-Paz (2021) that those methods with underspecified hyperparameter and model selection procedures are unhelpful. However, given that most neural methods have many more complexity hyperparameters than the single hyperparameter that we were able to carefully control for linear models, obtaining accurate tuning and reliable performance evaluation is likely to be a challenge. Gradient-based hyper-parameter estimation methods, as initially attempted in; , together with efficient methods for long-inner loop hypergradient calculation, may benefit the former problem by making search in larger numbers of hyperparameters more feasible. Alternatively, using general purpose pre-trained features as we did here, and focusing on learning shallow models that can be accurately tuned for DG may be another promising avenue in practice. Although achieving state of the art performance is not our focus, we note that our results in Table are quite competitive with end-to-end trained state of the art, despite using fixed features and shallow models.
Our analysis suggests that several existing methods that tried to tune learnable DG hyper-parameters by performance on a held-out domain (Balaji et al., 2018; Li et al., 2019) were broadly on the right track, and concurs with Gulrajani & Lopez-Paz (2021) that those methods with underspecified hyperparameter and model selection procedures are unhelpful
Answer: [Gulrajani & Lopez-Paz, 2020] observed that instance-wise was a better validation criterion than domain-wise. We have shown the opposite is the case when using better hyperparameter tuning.
Question: How do they evaluate how their model acquired words?
Evidence: Accuracy of acquired phoneme sequences representing the names of places
We evaluated whether the names of places were properly learned for the considered teaching places. This experiment assumes a request for the best phoneme sequence INLINEFORM0 representing the self-position INLINEFORM1 for a robot. The robot moves close to each teaching place. The probability of a word INLINEFORM2 when the self-position INLINEFORM3 of the robot is given, INLINEFORM4 , can be obtained by using equation ( EQREF37 ). The word having the best probability was selected. We compared the PAR with the correct phoneme sequence and a selected name of the place. Because “kiqchiN” and “daidokoro” were taught for the same place, the word whose PAR was the higher score was adopted.
Fig. FIGREF63 shows the results of PAR for the word considered the name of a place. SpCoA (latticelm), the proposed method using the results of unsupervised word segmentation on the basis of the speech recognition results in the lattice format, showed the best PAR score. In the 1-best and BoS methods, a part syllable sequence of the name of a place was more minutely segmented as shown in Table TABREF55 . Therefore, the robot could not learn the name of the teaching place as a coherent phoneme sequence. In contrast, the robot could learn the names of teaching places more accurately by using the proposed method.
Accuracy of acquired phoneme sequences representing the names of places
We evaluated whether the names of places were properly learned for the considered teaching places. This experiment assumes a request for the best phoneme sequence INLINEFORM0 representing the self-position INLINEFORM1 for a robot. The robot moves close to each teaching place. The probability of a word INLINEFORM2 when the self-position INLINEFORM3 of the robot is given, INLINEFORM4 , can be obtained by using equation ( EQREF37 ). The word having the best probability was selected. We compared the PAR with the correct phoneme sequence and a selected name of the place. Because “kiqchiN” and “daidokoro” were taught for the same place, the word whose PAR was the higher score was adopted.

Fig. FIGREF63 shows the results of PAR for the word considered the name of a place. SpCoA (latticelm), the proposed method using the results of unsupervised word segmentation on the basis of the speech recognition results in the lattice format, showed the best PAR score. In the 1-best and BoS methods, a part syllable sequence of the name of a place was more minutely segmented as shown in Table TABREF55 . Therefore, the robot could not learn the name of the teaching place as a coherent phoneme sequence. In contrast, the robot could learn the names of teaching places more accurately by using the proposed method.
Answer: The answers are shown as follows:
* PAR score
Question: How do you prepare lentil soup?
Answer: There are many ways to prepare lentil soup.  However a common method involves cooking onions, celery, and carrots in olive oil for several minutes.  Then you would add aromatics, such as garlic and spices, and cook for a minute.  Then you would add lentils and broth (or water) and bring to a simmer.  The soup should simmer for at least 30 minutes until the lentils are fully cooked.  Once finished cooking, you would garnish the soup with croutons, herbs, or drizzle with oil.
Question: Do they jointly tackle multiple tagging problems?
Evidence: We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag).
We select these tasks as examples for tagging applications because they differ strongly in tag set sizes. Generally, the Pos set sizes for all the languages are no more than 17 and Stag set sizes are around 200. When treating morphological features as a string (i.e. not splitting into key-value pairs), the sizes of the Morph tag sets range from about 100 up to 2000.
The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN.
We evaluate our method on three tagging tasks: POS tagging (Pos), morphological tagging (Morph) and supertagging (Stag).
We select these tasks as examples for tagging applications because they differ strongly in tag set sizes.
The test results for the three tasks are shown in Table TABREF17 in three groups. The first group of seven columns are the results for Pos, where both LSTM and CNN have three variations of input features: word only ( INLINEFORM0 ), character only ( INLINEFORM1 ) and both ( INLINEFORM2 ). For Morph and Stag, we only use the INLINEFORM3 setting for both LSTM and CNN.
Answer: No.
Question: Given the reference text, what is the female equivalent of Karl?
Evidence: Carol is used as a female name, Carl or Karl is the male version of the name, although in contemporary Western usage it is more commonly used for females. It is a European continental spelling of the English Charles, the Germanic Carl, and the Latin Carolus, and can also be a short version of the name Caroline. Spelling variations include Carole.
Answer: Carol (sometimes Carole and other variations) is used a female name for Carl or Karl.
Question: What definition was one of the groups was shown?
Evidence: In order to assess the reliability of the hate speech definitions on social media more comprehensively, we developed two online surveys in a between-subjects design. They were completed by 56 participants in total (see Table TABREF7 ). The main goal was to examine the extent to which non-experts agree upon their understanding of hate speech given a diversity of social media content. We used the Twitter definition of hateful conduct in the first survey. This definition was presented at the beginning, and again above every tweet. The second survey did not contain any definition. Participants were randomly assigned one of the two surveys.
We used the Twitter definition of hateful conduct in the first survey. This definition was presented at the beginning, and again above every tweet.
Answer: The answers are shown as follows:
* Twitter definition of hateful conduct
Question: Is Slovenia a nice place to take a food fueled vacation?
Answer: Yeah, for sure, Slovenia regularly ranks super-highly in terms of safety, sustainability, and friendliness. The whole country was also awarded the “European Region of Gastronomy” title for 2021, and a few years before that, chef Ana Roš was named World’s Best Female Chef
Question: Give me some examples of Cockney Rhyming Slang and help me understand how it works.
Answer: Cockney rhyming slang is a slang language originating from the East End in London, United Kingdom. It has a simple structure where a rhyming word or phrase is used in substitution of the word's true meaning. Examples:

Apples and pears = stairs
Adam and Eve = believe
Barnaby Rudge = judge
Bees and Honey = money
Dog and Bone = Phone
Hank Marvin = starving
Lady Godiva = fiver
Question: Was there an industrial revolution during the middle ages?
Answer: Most economic historians would argue that an industrial revolution would require widespread use of coal for heating, mechanical power generation, and metal making.  In that sense, there was no medieval industrial revolution.  However, widespread investment in windmills, water wheels, and canals did help the European economy grow and improve the living standards of the average medieval peasant.  Similarly, cross border trade in bulk commodities like wool and wine allowed for an international division of labor, with the associated improvement in productivity and living standards.  By the end of the Middle Ages, mechanical clocks had become ubiquitous: plenty of craftsmen had the precision metalworking skills to build these complex machines, and even more people could afford to buy them.  Not quire an industrial revolution, but certainly a prosperous society taking the first steps towards automation.
Question: Which of the following countries do not follow the rectangular-shaped flag standard that most every other country does: Nepal, South Africa, Japan, South Korea, Vatican City, Switzerland, Vietnam, Australia
Answer: Nepal, Vatican City, Switzerland
Question: What are the primary features of a gravity hill given the following description?
Evidence: A gravity hill, also known as a magnetic hill, mystery hill, mystery spot, gravity road, or anti-gravity hill, is a place where the layout of the surrounding land produces an optical illusion, making a slight downhill slope appear to be an uphill slope. Thus, a car left out of gear will appear to be rolling uphill against gravity. Hundreds of gravity hills are recognized worldwide.
Answer: The gravity hill optical illusion is defined by having the following characteristics:

1. A downhill slope will appear to be an uphill slope
2.  Objects appear to be rolling uphill against gravity
Question: Which of the following drinks are considered safe for children? Beer, cocktail, coffee, tea, boba, juice, water, milk, coke, lemonade.
Answer: Water, lemonade, milk, and juice are generally safe for children, even though excessive intake of lemonade / juice can lead to obesity / dental problems. Coffee, tea, boba and coke should be limited (if not completely avoided) since they contain caffeine. Alcoholic drinks like beer and cocktail are not safe for children and should be avoided completely.
Question: What is the difference between a lion and a tiger?
Answer: Lions and tigers are species in the same family, Felidae. They have different appearances, habitats, social behaviors, and hunting tendencies. For example, lions are a social species and will hunt and live in groups, called prides. Tigers, on the other hand, are independent and only interact with other tigers for mating purposes.
Question: What corpora do the authors make both intrinsic and extrinsic analysis?
Evidence: Four publicly available corpora are selected for our analysis. These corpora are based on Wikipedia, so more comparable than the others, and have already been used for the evaluation of several QA systems.
WikiQA BIBREF6 comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles. The abstracts of these articles are then extracted to create answer candidates. The assumption is made that if many queries lead to the same article, it must contain the answer context; however, this assumption fails for some occasions, which makes this dataset more challenging. Since the existence of answer contexts is not guaranteed in this task, it is called answer triggering instead of answer selection.
SelQA BIBREF7 is a product of five annotation tasks through crowdsourcing. It consists of about 8K questions where a half of the questions are paraphrased from the other half, aiming to reduce contextual similarities between questions and answers. Each question is associated with a section in Wikipedia where the answer context is guaranteed, and also with five sections selected from the entire Wikipedia where the selection is made by the Lucene search engine. This second dataset does not assume the existence of the answer context, so can be used for the evaluation of answer triggering.
SQuAD BIBREF12 presents 107K+ crowdsourced questions on 536 Wikipedia articles, where the answer contexts are guaranteed to exist within the provided paragraph. It contains annotation of answer phrases as well as the pointers to the sentences including the answer phrases; thus, it can be used for both answer extraction and selection. This corpus also provides human accuracy on those questions, setting up a reasonable upper bound for machines. To avoid overfitting, the evaluation set is not publicly available although system outputs can be evaluated by their provided script.
InfoboxQA BIBREF13 gives 15K+ questions based on the infoboxes from 150 articles in Wikipedia. Each question is crowdsourced and associated with an infobox, where each line of the infobox is considered an answer candidate. This corpus emphasizes the gravity of infoboxes, which summary arguably the most commonly asked information about those articles. Although the nature of this corpus is different from the others, it can also be used to evaluate answer selection.
WIKIQA (Yang et al., 2015) comprises questions selected from the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles.
Answer: It is the Bing search queries, where user click data give the questions and their corresponding Wikipedia articles.
Question: What are the Multi-party Conversational Systems?
Evidence: Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems. From the moment that an utterance is sent to a group, to the moment that it is replied in the group by a member, several activities must be done by the system: utterance understanding, information search, reasoning, among others. In this paper we present the challenges of designing and building multi-party conversational systems, the state of the art, our proposed hybrid architecture using both rules and machine learning and some insights after implementing and evaluating one on the finance domain.
Multi-party Conversational Systems are systems with natural language interaction between one or more people or systems.
Answer: Systems with natural language interaction between one or more people or systems.
Question: Tell me whether the following animals are part of the cat family: ocelot, lion, jackal, caracal, leopard, fox, tiger, lynx.
Answer: Ocelot, lion, caracal, leopard, tiger, lynx are all species that are part of the Felidae family (commonly known as cats). Jackals and foxes are part of the Canidae (or dog) family. While the two groups have physical similarities, they also have distinctive physical and behavioral differences.
Question: did they compare with other evaluation metrics?
Evidence: FLOAT SELECTED: Figure 5: Comparison of nex-cv and Human-Rater Accuracy. The six datasets from pseudonymous chatbots tested had a different number of questions (examples) and categories (classes), as shown in the bottom row. The human-rater estimate of accuracy (top left, blue) is consistently more lenient than any of the automated measures (top right). The (0; 0.15) setting (top right, blue) is not consistently more or less optimistic than the other settings.
FLOAT SELECTED: Figure 5: Comparison of nex-cv and Human-Rater Accuracy. The six datasets from pseudonymous chatbots tested had a different number of questions (examples) and categories (classes), as shown in the bottom row. The human-rater estimate of accuracy (top left, blue) is consistently more lenient than any of the automated measures (top right). The (0; 0.15) setting (top right, blue) is not consistently more or less optimistic than the other settings.
Answer: Yes.
Question: What was the app after the second app released by YouTube?
Evidence: Through this period, YouTube tried several new ways to generate revenue beyond advertisements. In 2013, YouTube launched a pilot program for content providers to offer premium, subscription-based channels. This effort was discontinued in January 2018 and relaunched in June, with US$4.99 channel subscriptions. These channel subscriptions complemented the existing Super Chat ability, launched in 2017, which allows viewers to donate between $1 and $500 to have their comment highlighted. In 2014, YouTube announced a subscription service known as "Music Key," which bundled ad-free streaming of music content on YouTube with the existing Google Play Music service. The service continued to evolve in 2015 when YouTube announced YouTube Red, a new premium service that would offer ad-free access to all content on the platform (succeeding the Music Key service released the previous year), premium original series, and films produced by YouTube personalities, as well as background playback of content on mobile devices. YouTube also released YouTube Music, a third app oriented towards streaming and discovering the music content hosted on the YouTube platform.
Answer: The third app released by YouTube was YouTube Music, which oriented towards streaming and discovering the music content hosted on the YouTube platform
Question: What are the most common types of pizzas?
Answer: There are lots of different pizzas, but the most common are cheese and pepperoni. There are also different types of pizza such as thin crust, New York style pizza, and Chicago style pizza.
Question: What are some good quality movies I can watch?
Answer: Moonlight, Everything Everywhere All at Once, Gone with the Wind and Chicago all won Oscars for Best Picture. You could start with those.
Question: What might be the reason why deictic scores are less sensitive to training data than lexical cohesion and ellipsis scores?
Evidence: Table TABREF33 provides BLEU and consistency scores for the DocRepair model trained on different amount of data. We see that even when using a dataset of moderate size (e.g., 5m fragments) we can achieve performance comparable to the model trained on a large amount of data (30m fragments). Moreover, we notice that deixis scores are less sensitive to the amount of training data than lexical cohesion and ellipsis scores. The reason might be that, as we observed in our previous work BIBREF11, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies. Also, as we show in Section SECREF7, this is the phenomenon the model learns faster in training.
Moreover, we notice that deixis scores are less sensitive to the amount of training data than lexical cohesion and ellipsis scores. The reason might be that, as we observed in our previous work, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies.
Answer: The reason might be that, as they observed in their previous work, inconsistencies in translations due to the presence of deictic words and phrases are more frequent in this dataset than other types of inconsistencies.
Question: What type of RNN is used?
Evidence: Social media mining is considerably an important source of information in many of health applications. This working note presents RNN and LSTM based embedding system for social media health text classification. Due to limited number of tweets, the performance of the proposed method is very less. However, the obtained results are considerable and open the way in future to apply for the social media health text classification. Moreover, the performance of the LSTM based embedding for task 2 is good in comparison to the task 1. This is primarily due to the fact that the target classes of task 1 data set imbalanced. Hence, the proposed method can be applied on large number of tweets corpus in order to attain the best performance.
Recently, the deep learning methods have performed well BIBREF8 and used in many tasks mainly due to that it doesn't rely on any feature engineering mechanism. However, the performance of deep learning methods implicitly relies on the large amount of raw data sets. To make use of unlabeled data, BIBREF9 proposed semi-supervised approach based on Convolutional neural network for adverse drug event detection. Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method.
This working note presents RNN and LSTM based embedding system for social media health text classification.
Though the data sets of task 1 and task 2 are limited, this paper proposes RNN and LSTM based embedding method.
Answer: The answers are shown as follows:
* RNN
* LSTM