DataHammer/scimrc huggingface dataset:
Question: Do the authors suggest why syntactic parsing is so important for semantic role labelling for interlanguages?
Evidence: While the neural syntax-agnostic system obtains superior performance on the L1 data, the two syntax-based systems both produce better analyses on the L2 data. Furthermore, as illustrated in the comparison between different parsers, the better the parsing results we get, the better the performance on L2 we achieve. This shows that syntactic parsing is important in semantic construction for learner Chinese. The main reason, according to our analysis, is that the syntax-based system may generate correct syntactic analyses for partial grammatical fragments in L2 texts, which provides crucial information for SRL. Therefore, syntactic parsing helps build more generalizable SRL models that transfer better to new languages, and enhancing syntactic parsing can improve SRL to some extent.
While the neural syntax-agnostic system obtains superior performance on the L1 data, the two syntax-based systems both produce better analyses on the L2 data. Furthermore, as illustrated in the comparison between different parsers, the better the parsing results we get, the better the performance on L2 we achieve. This shows that syntactic parsing is important in semantic construction for learner Chinese. The main reason, according to our analysis, is that the syntax-based system may generate correct syntactic analyses for partial grammatical fragments in L2 texts, which provides crucial information for SRL.
Answer: The answers are shown as follows:
* syntax-based system may generate correct syntactic analyses for partial grammatical fragments
Question: What is dihydrogen monoxide?
Answer: Dihydrogen monoxide, the molecule composed of two hydrogen atoms and one oxygen atom, is a stable substance that is a clear, colorless, odorless liquid at room temperature. It is almost always referred to as "water" instead of "dihydrogen monoxide." It is abundant on Earth and a vital nutrient to all living things.
Question: What is an ice pop?
Evidence: An ice pop is a liquid-based frozen snack on a stick. Unlike ice cream or sorbet, which are whipped while freezing to prevent ice crystal formation, an ice pop is "quiescently" frozen—frozen while at rest—and becomes a solid block of ice. The stick is used as a handle to hold it. Without a stick, the frozen product would be a freezie.

An ice pop is also referred to as a popsicle in Canada and the United States, flash in Algeria, paleta in Mexico, the Southwestern United States and parts of Latin America, ice lolly in the United Kingdom (the term ice pop refers to a freezie in the United Kingdom), ice drop in the Philippines, ice gola in India, ice candy in India and Japan, ai tim tang or ice cream tang in Thailand (though both words is also colloquially used to refer to ice cream bar), and kisko in the Caribbean. The term icy pole is often used in Australia, but is a brand name for a specific type, so ice block is also used.
Answer: An ice-pop is a frozen dessert. It is served on a stick, and is made from sweetened liquid that frozen as a block of ice.
Question: What contribution does the author make in this paper?
Evidence: In this paper, we presented a novel way how to evaluate different interactive learning approaches for dialog models. The evaluation covers two challenging aspects of interactive learning. First, it scores efficiency of using information gained from users in simulated question answering dialogs. Second, it measures accuracy on answer hints understanding.
For purposes of evaluation we collected a dataset from conversational dialogs with workers on crowdsourcing platform CrowdFlower. Those dialogs were annotated with expert annotators and published under Creative Commons 4.0 BY-SA license on lindat. We also provide evaluation scripts with the dataset that should ensure comparable evaluation of different interactive learning approaches.
In this paper, we presented a novel way how to evaluate different interactive learning approaches for dialog models. The evaluation covers two challenging aspects of interactive learning. First, it scores efficiency of using information gained from users in simulated question answering dialogs. Second, it measures accuracy on answer hints understanding.
Answer: In this paper, they presented a novel way how to evaluate different interactive learning approaches for dialog models. The evaluation covers two challenging aspects of interactive learning. First, it scores efficiency of using information gained from users in simulated question answering dialogs. Second, it measures accuracy on answer hints understanding.
Question: Does Julie Andrews have an EGOT?
Answer: No, to satisfy the requirements of being an actor or actress who has achieved the distinction of having an "EGOT" you must have won an Emmy, a Grammy, an Oscar, and a Tony Award. Julie Andrews has won an Emmy, a Grammy, and an Oscar; however, she has never won a Tony Award despite being nominated three times.
Question: Who and when was the compass invented?
Answer: Over 2000 years ago by the Chinese, by magnetizing needles made from iron, and placing them in water
Question: what is a project milestone?
Answer: A project milestone is one group of milestones that have been achieved that help to get a project or use case completed and in production.  Milestones are important and help show a project is on track and in budget.  Project milestones are also a checkpoint to see if you need to recycle to a different phase of a project or take a different direction too.
Question: Is Leucascidae a sponge
Answer: Leucascidae is a family of calcareous sponges in the order Clathrinida.
Question: What is the typical color of each of following objects: stop sign, school bus, traffic cone, pickle, banana, tooth?
Answer: stop sign: red
school bus: yellow
traffic cone: orange
pickle: green
banana: yellow
tooth: white
Question: In the following passage about mechanical-switch keyboards, which keyboards are the loudest, and how can I make them quieter?
Evidence: Every key on a mechanical-switch keyboard contains a complete switch underneath. Each switch is composed of a housing, a spring, and a stem, and sometimes other parts such as a separate tactile leaf or a clickbar. Switches come in three variants: "linear" with consistent resistance, "tactile" with a non-audible bump, and "clicky" with both a bump and an audible click. Depending on the resistance of the spring, the key requires different amounts of pressure to actuate and to bottom out. The shape of the stem as well as the design of the switch housing varies the actuation distance and travel distance of the switch. The sound can be altered by the material of the plate, case, lubrication, the keycap profile, and even modifying the individual switch. These modifications, or "mods" include applying lubricant to reduce friction inside the switch itself, inserting "switch films" to reduce wobble, swapping out the spring inside to modify the resistance of the switch itself and many more. Mechanical keyboards allow for the removal and replacement of keycaps with a common stem type.
Answer: Mechanical-switch keyboards with "Clicky" switches come with both a bump and an audible click. Without adjusting the material of the plates or case, the way for users to reduce the noise would be to replace the keycaps or applying lubricant to reduce friction inside the switch.
Question: What are some things that we can do to help the environment?
Answer: - Minimize the use of plastics
- Use public transport instead of personal vehicle
- Carpooling
- Buy only what you need
- Buy products made of recycled materials
- Recycle any plastics, glass or metals
- Turn off and electrical appliance when not in use
- Use reusable straws instead of plastic straws
- Use energy efficient electrical appliance
- Buy an electric car
Question: What are some ways to tenderize beef?
Answer: There are several techniques used to tenderize beef. These include the following:
1. Pounding the beef with a mallet 
2. Seasoning the beef with salt and allowing it to rest for a while
3. A good approach for tenderizing beef for stir frying beef to cut it into thin strips and coating it with cornstarch
Question: What are the pros and cons of working from home?
Answer: The pros might be no commute, increase in productivity, more comfortable setting, easier to collaborate with others across geographies. While the cons might be more distractions, challenges maintaining boundaries and separating work from home, lack of camaraderie or team culture.
Question: Why do people need money?
Answer: People need money to satisfy their basic needs such as food, clothing and shelter so they lead a humane life. It plays a critical role in education as many studies show that it has a proportional relation between household income and children’s educational level, which ties to their success post-education in society. Money also provides opportunities to enrich lifestyes and enhance well-being. With its importance, people often fight for it, and money sadly causes crimes.
Question: What are AirPods?
Answer: Air Pods are an in ear headphone product made and sold by Apple. Their unique market differentiators are a software simulated spatial audio system and noise canceling features.
Question: What is the Jones-Connally Act?
Evidence: The Jones–Connally Act was a New Deal Initiative passed by Congress in April 1934 as an extension to the Agricultural Adjustment Act. Largely in response to the great drought of 1933–1934, cattle ranchers acted against their former opposition to the commodification of cattle and appealed to the government for assistance in ridding of themselves of the millions of cattle they could no longer afford to feed or to keep alive without a loss on return.
Answer: The Jones–Connally Act was passed by the US Congress in April 1934.  It was an extension to the Agricultural Adjustment Act.  It was part of the New Deal and was in response to the drought of 1933-1934.  It made cattle a basic commodity giving the government authority over the distribution and processing of the cattle for public relief purposes.
Question: Which dialogue acts are more suited to the twitter domain?
Evidence: In this work, we are motivated to predict the dialogue acts in conversations with the intent of identifying problem spots that can be addressed in real-time, and to allow for post-conversation analysis to derive rules about conversation outcomes indicating successful/unsuccessful interactions, namely, customer satisfaction, customer frustration, and problem resolution. We focus on analysis of the dialogue acts used in customer service conversations as a first step to fully automating the interaction. We address various different challenges: dialogue act annotated data is not available for customer service on Twitter, the task of dialogue act annotation is subjective, existing taxonomies do not capture the fine-grained information we believe is valuable to our task, and tweets, although concise in nature, often consist of overlapping dialogue acts to characterize their full intent. The novelty of our work comes from the development of our fine-grained dialogue act taxonomy and multi-label approach for act prediction, as well as our analysis of the customer service domain on Twitter. Our goal is to offer useful analytics to improve outcome-oriented conversational systems.
. We address various different challenges: dialogue act annotated data is not available for customer service on Twitter, the task of dialogue act annotation is subjective, existing taxonomies do not capture the fine-grained information we believe is valuable to our task, and tweets, although concise in nature, often consist of overlapping dialogue acts to characterize their full intent.
Answer: The answers are shown as follows:
* overlapping dialogue acts
Question: What are the different swimming styles? Which one is the fastest and which one the slowest?
Answer: There are four main different swimming styles in competitive swimming. These are backstroke, breaststroke, butterfly and crawl.

Crawl is the fastest style, while breaststroke is considered to be the slowest.
Question: Identify the bird from the list: Pendulum, Bewick's swan, Iceberg
Answer: Bewick's swan
Question: Is audio data per language balanced in dataset?
Evidence: The data presented in Table (TABREF12) shows the currently available data. Each of the released languages is available for individual download as a compressed directory from the Mozilla Common Voice website. The directory contains six files with Tab-Separated Values (i.e. TSV files), and a single clips subdirectory which contains all of the audio data. Each of the six TSV files represents a different segment of the voice data, with all six having the following column headers: [client_id, path, sentence, up_votes, down_votes, age, gender, accent]. The first three columns refer to an anonymized ID for the speaker, the location of the audio file, and the text that was read. The next two columns contain information on how listeners judged the $<$audio,transcript$>$ pair. The last three columns represent demographic data which was optionally self-reported by the speaker of the audio.
FLOAT SELECTED: Table 1: Current data statistics for Common Voice. Data in italics is as of yet unreleased. Other numbers refer to the data published in the June 12, 2019 release.
We made dataset splits (c.f. Table (TABREF19)) such that one speaker's recordings are only present in one data split. This allows us to make a fair evaluation of speaker generalization, but as a result some training sets have very few speakers, making this an even more challenging scenario. The splits per language were made as close as possible to 80% train, 10% development, and 10% test.
FLOAT SELECTED: Table 2: Data used in the experiments, from an earlier multilingual version of Common Voice. Number of audio clips and unique speakers.
The data presented in Table (TABREF12) shows the currently available data. Each of the released languages is available for individual download as a compressed directory from the Mozilla Common Voice website. 
FLOAT SELECTED: Table 1: Current data statistics for Common Voice. Data in italics is as of yet unreleased. Other numbers refer to the data published in the June 12, 2019 release.
We made dataset splits (c.f. Table (TABREF19)) such that one speaker's recordings are only present in one data split. 
FLOAT SELECTED: Table 2: Data used in the experiments, from an earlier multilingual version of Common Voice. Number of audio clips and unique speakers.
Answer: No.
Question: Given this paragraph about Fyre Festival, would you say that it was a successful event?
Evidence: Fyre Festival was a fraudulent luxury music festival founded by con artist Billy McFarland and rapper Ja Rule. It was created with the intent of promoting the company's Fyre app for booking music talent. The festival was scheduled to take place on April 28–30 and May 5–7, 2017, on the Bahamian island of Great Exuma.
Answer: No, Fyre Festival was a fraudulent luxury music festival founded by con artist Billy McFarland and rapper Ja Rule
Question: Which is the baseline model?
Evidence: As the first step, we build three baseline LID systems, one based on the i-vector model, and the other two based on LSTM-RNN, using the speech data of two languages from Babel: Assamese and Georgian (AG).
The two RNN LID baselines are: a standard RNN LID system (AG-RNN-LID) that discriminates between the two languages in its output, and a multi-task system (AG-RNN-MLT) that was trained to discriminate between the two languages as well as the phones. More precisely, the output units of the AG-RNN-MLT are separated into two groups: an LID group that involves two units corresponding to Assamese and Georgian respectively, and an ASR group that involves $3,349$ bilingual senones that are inherited from an HMM/GMM ASR system trained with the speech data of Assamese and Georgian, following the standard WSJ s5 HMM/GMM recipe of Kaldi. The WSJ s5 nnet3 recipe of Kaldi is then used to train the AG-RNN-LID and AG-RNN-MLT systems.
As the first step, we build three baseline LID systems, one based on the i-vector model, and the other two based on LSTM-RNN, using the speech data of two languages from Babel: Assamese and Georgian (AG).
The two RNN LID baselines are: a standard RNN LID system (AG-RNN-LID) that discriminates between the two languages in its output, and a multi-task system (AG-RNN-MLT) that was trained to discriminate between the two languages as well as the phones.
Answer: The three baseline models are the i-vector model, a standard RNN LID system and a multi-task RNN LID system.
Question: Why is Peter Pan usually portrayed by a female on stage performances (not movies)?
Answer: Peter Pan was introduced to the world in 1904. At the time, stage productions were prominent and the Broadway play director wanted to cast a female. That tradition has continued and a woman is cast often.
Question: Is AI a risk to humanity?
Answer: Artificial Intelligence (AI) is just a tool. Like all tools made by humans throughout the history it is neither good nor bad. It is neither safe nor risky. The humans who control and use it will determine those. Looking at history, all past human technological developments lead to improved human quality of life. AI is likely going to significantly improve human quality of life.
Question: Do they use the same architecture as LSTM-s and GRUs with just replacing with the LAU unit?
Evidence: For all experiments, the dimensions of word embeddings and recurrent hidden states are both set to 512. The dimension of INLINEFORM0 is also of size 512. Note that our network is more narrow than most previous work where hidden states of dimmention 1024 is used. we initialize parameters by sampling each element from the Gaussian distribution with mean 0 and variance INLINEFORM1 .
For all experiments, the dimensions of word embeddings and recurrent hidden states are both set to 512.
Answer: Yes.
Question: What is the dominant character encoding for the World Wide Web?
Evidence: UTF-8 is the dominant encoding for the World Wide Web (and internet technologies), accounting for 97.9% of all web pages, over 99.0% of the top 10,000 pages, and up to 100.0% for many languages, as of 2023. Virtually all countries and languages have 95.0% or more use of UTF-8 encodings on the web.
Answer: The dominant character encoding for the World Wide Web is UTF-8. It is used by 97.9% of all web pages.
Question: Where do black walnut trees grow?
Evidence: Juglans nigra, the eastern American black walnut, is a species of deciduous tree in the walnut family, Juglandaceae, native to North America. It grows mostly in riparian zones, from southern Ontario, west to southeast South Dakota, south to Georgia, northern Florida and southwest to central Texas. Wild trees in the upper Ottawa Valley may be an isolated native population or may have derived from planted trees.
Answer: North America from southern Ontario, west to southeast South Dakota, south to Georgia, northern Florida and southwest to central Texas.
Question: How does the Transformer propagate information within and across sentences in NMT?
Evidence: In NMT, the Transformer BIBREF0 is a sequence-to-sequence (seq2seq) model which maps an input sequence to an output sequence through hierarchical multi-head attention mechanisms, yielding a dynamic, context-dependent strategy for propagating information within and across sentences. It contrasts with previous seq2seq models, which usually rely either on costly gated recurrent operations BIBREF15, BIBREF16 or static convolutions BIBREF17.
Given $n$ query contexts and $m$ sequence items under consideration, attention mechanisms compute, for each query, a weighted representation of the items. The particular attention mechanism used in BIBREF0 is called scaled dot-product attention, and it is computed in the following way:
where $\mathbf {Q} \in \mathbb {R}^{n \times d}$ contains representations of the queries, $\mathbf {K}, \mathbf {V} \in \mathbb {R}^{m \times d}$ are the keys and values of the items attended over, and $d$ is the dimensionality of these representations. The $\mathbf {\pi }$ mapping normalizes row-wise using softmax, $\mathbf {\pi }(\mathbf {Z})_{ij} = \operatornamewithlimits{\mathsf {softmax}}(\mathbf {z}_i)_j$, where
In words, the keys are used to compute a relevance score between each item and query. Then, normalized attention weights are computed using softmax, and these are used to weight the values of each item at each query context.
However, for complex tasks, different parts of a sequence may be relevant in different ways, motivating multi-head attention in Transformers. This is simply the application of Equation DISPLAY_FORM7 in parallel $H$ times, each with a different, learned linear transformation that allows specialization:
In the Transformer, there are three separate multi-head attention mechanisms for distinct purposes:
Encoder self-attention: builds rich, layered representations of each input word, by attending on the entire input sentence.
Context attention: selects a representative weighted average of the encodings of the input words, at each time step of the decoder.
Decoder self-attention: attends over the partial output sentence fragment produced so far.
Together, these mechanisms enable the contextualized flow of information between the input sentence and the sequential decoder.
In NMT, the Transformer (Vaswani et al., 2017) is a sequence-to-sequence (seq2seq) model which maps an input sequence to an output sequence through hierarchical multi-head attention mechanisms, yielding a dynamic, context-dependent strategy for propagating information within and across sentences.
Answer: It maps an input sequence to an output sequence through hierarchical multi-head attention mechanisms, yielding a dynamic, context-dependent strategy for propagating information within and across sentences.
Question: Extract the names of the countries that has occurrence of Stereospermum kunthianum from the text. Separate them with a comma.
Evidence: Stereospermum kunthianum is an African deciduous shrub or small tree occurring in the Democratic Republic of Congo, Djibouti, Eritrea, Ethiopia, Kenya, Malawi, Senegal, Somalia, Sudan, Tanzania and Uganda. It is widespread across Africa to the Red Sea, and reaches as far south as Angola, Mozambique, Zambia and Zimbabwe. There are some 30 species with a Central African and Asian distribution.
Answer: Democratic Republic of Congo, Djibouti, Eritrea, Ethiopia, Kenya, Malawi, Senegal, Somalia, Sudan, Tanzania, Uganda.
Question: What's the function of the proposed model?
Evidence: In this paper, we present a deep neural network model for the task of fine-grained named entity classification using ELMo embeddings and Wikidata. The proposed model learns representations for entity mentions based on its context and incorporates the rich structure of Wikidata to augment these labels into finer-grained subtypes. We can see comparisons of our model made on Wiki(gold) in Table TABREF20 . We note that the model performs similarly to existing systems without being trained or tuned on that particular dataset. Future work may include refining the clustering method described in Section 2.2 to extend to types other than person, location, organization, and also to include disambiguation of entity types.
The proposed model learns representations for entity mentions based on its context and incorporates the rich structure of Wikidata to augment these labels into finer-grained subtypes.
Answer: The proposed model learns representations for entity mentions based on its context and incorporates the rich structure of Wikidata to augment these labels into finer-grained subtypes.
Question: Classify each of the following as a dental or hair product: toothbrush, floss, shampoo, conditioner, gel, floss
Answer: Toothbrush: Dental Product
Floss: Dental Product
Shampoo: Hair Product
Conditioner: Hair Product
Gel: Hair Product
Floss: Dental Product