DataHammer/scimrc huggingface dataset:
Question: Which characters belong to DC or Marvel Universe? Iron Man, Batman, Steel
Answer: Batman and Steel are DC, Iron Man is Marvel
Question: Which major geographical regions are studied?
Evidence: We examine other features regarding the characteristics of the studied colleges, which might be significant factors of sexual harassment. Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest). We also use the normalized rape-related cases count (number of cases reported per student enrolled) from the stated government resource as another attribute to examine the proximity of our dataset to the official one. This feature vector is then fitted in a linear regression to predict the normalized #metoo users count (number of unique users who posted #MeToo tweets per student enrolled) for each individual college.
Four factual attributes pertaining to the 200 colleges are extracted from the U.S. News Statistics, which consists of Undergraduate Enrollment, Male/Female Ratio, Private/Public, and Region (Northeast, South, West, and Midwest). 
Answer: Northeast U.S, South U.S., West U.S. and Midwest U.S.
Question: Can the method effectively mitigate the bias pattern and improve the cross-dataset generalization ability of models?
Evidence: Natural language inference (NLI) is a widely-studied problem in natural language processing. It aims at comparing a pair of sentences (i.e. a premise and a hypothesis), and inferring the relationship between them (i.e., entailment, neutral and contradiction). Large-scaled datasets like SNLI BIBREF0 and MultiNLI BIBREF1 have been created by crowd-sourcing and fertilized NLI research substantially.
However, several works BIBREF2, BIBREF3, BIBREF4 have pointed out that crowd-sourcing workers have brought a bias pattern named annotation artifacts in these NLI datasets. Such artifacts in hypotheses can reveal the labels and make it possible to predict the labels solely by looking at the hypotheses. For example, models trained on SNLI with only the hypotheses can achieve an accuracy of 67.0%, despite the always predicting the majority-class baseline is only 34.3% BIBREF2.
Classifiers trained on NLI datasets are supposed to make predictions by understanding the semantic relationships between given sentence pairs. However, it is shown that models are unintentionally utilizing the annotation artifacts BIBREF4, BIBREF2. If the evaluation is conducted under a similar distribution as the training data, e.g., with the given testing set, models will enjoy additional advantages, making the evaluation results over-estimated. On the other hand, if the bias pattern cannot be generalized to the real-world, it may introduce noise to models, thus hurting the generalization ability.
In this paper, we use cross-dataset testing to better assess models' generalization ability. We investigate the impacts of annotation artifacts in cross-dataset testing. Furthermore, we propose an easy-adopting debiasing training framework, which doesn't require any additional data or annotations, and apply it to the high-performing Densely Interactive Inference Network BIBREF5. Experiments show that our method can effectively mitigate the bias pattern and improve the cross-dataset generalization ability of models. To the best of our knowledge, our work is the first attempt to alleviate the annotation artifacts without any extra resources.
Experiments show that our method can effectively mitigate the bias pattern and improve the cross-dataset generalization ability of models.
Answer: Yes, they can.
Question: Can dogs look up?
Answer: Despite what the character "Big Al" in the 2004 Horror Comedy "Shaun of the Dead" thought, dogs can, indeed, look up. The film is responsible for perpetuating the myth even though in context the quote is meant to throw doubt on the validity of anything that "Big Al" says.
Question: Assuming c in Eq. (1) denotes the labels; did you mean the loss \ell(f(X), c) is a convex function and not affine?
Evidence: A kernel machine uses the kernel trick to map the non-linear problem into a feature space where the problem may be linearly separable with an appropriate kernel function. Next we give the formal definition of kernel machines discussed in this paper. Given a training data set {X, y} of n training instances where {X ∈ R n×d , y ∈ R n } = {(x 1 , y 1 ), (x 2 , y 2 ), ..., (x n , y n )}, and (x i , y i ) denotes the instance x i ∈ R d with its label y i , the objective of the kernel machine training is to find an optimal ω * which minimizes the structural risk as follows.
min
where λ denotes the regularization constant and f (ω, x i ) = ω, φ(x i ) . The variable ω is defined on the reproducing kernel Hilbert space (RKHS) and •, • is the inner product on the RKHS. The function φ(•) maps the instances from their original data space to a higher dimensional feature space induced by the kernel function. Assume the loss l(•, •) is an affine function of ω. The representer theorem shows that a minimizer of the optimization problem ()
where k(x i , x j ) denotes a positive definite kernel function and k(x i , x j ) = φ(x i ), φ(x j ) . By substituting the expressions of f (ω, x i ) and ω into the Equation (1), we have the objective with respect to α below.
where α = [α 1 . . . α n ] T is an n-dimension vector, each dimension of which corresponds to the contribution of a training instance to the kernel machine.
Then, we can derive that the Hessian matrix H = [H ij ] n×n of Problem () is equal to the kernel matrix. The element in the i-th row and j-th column of the matrix H is H ij = k(x i , x j ). The derivations of the first and second derivatives can be found in the supplementary material. For clarity, we use kernel matrix H to denote the Hessian matrix of the kernel machine in the rest of the paper.
Assume the loss l(·, ·) is an affine function of ω.
Answer: The loss is assumed to be a convex and affine function. We've reorganized the equations in Section 2 for clarity.
Question: What should I get my wife for mother's day?
Answer: Mother's day is a great opportunity for a thoughtful gift. If she has a car, getting her car washed can be a nice surprise. You can pair that with a nice meal out, but instead of having her pick the place, give two or three suggestions and let her choose. If she often plans the meals, asking her to just pick the places without options is shifting the cognitive load to her and not a gift if she already plans the meals daily. If you would a gift to wrap and are not sure what she would like, this is where you can phone a friend or female family member and see if they have any suggestions for gifts. If none of these work for you, go to a store that she likes and around mother's days most stores will have items marketed as mother's day gifts. If that is still too much, Amazon will have holiday specific gifts and you can include gift  wrapping on check out.
Question: Can you please help me with a couple of points about analgesic drug, with the given text as a reference.
Evidence: An analgesic drug, also called simply an analgesic (American English), analgaesic (British English), pain reliever, or painkiller, is any member of the group of drugs used to achieve relief from pain (that is, analgesia or pain management). Analgesics are conceptually distinct from anesthetics, which temporarily reduce, and in some instances eliminate, sensation, although analgesia and anesthesia are neurophysiologically overlapping and thus various drugs have both analgesic and anesthetic effects.
Answer: 1. Any medication from the class of medications intended to treat pain is referred to as an analgesic medicine, also known as a simple analgesic (American English), an analgaesic (British English), pain reliever, or painkiller. (that is, analgesia or pain management).
2. Although analgesia and anaesthesia are neurophysiologically similar and thus certain medications have both analgesic and anaesthetic effects, analgesics are conceptually separate from anesthetics, which temporarily diminish, and in some cases eradicate, feeling.
Question: What four transcripts of the 2016 US election were used?
Evidence: We created a new dataset called CW-USPD-2016 (check-worthiness in the US presidential debates 2016) for finding check-worthy claims in context. In particular, we used four transcripts of the 2016 US election: one vice-presidential and three presidential debates. For each debate, we used the publicly-available manual analysis about it from nine reputable fact-checking sources, as shown in Table TABREF7. This could include not just a statement about factuality, but any free text that journalists decided to add, e.g., links to biographies or behavioral analysis of the opponents and moderators. We converted this to binary annotation about whether a particular sentence was annotated for factuality by a given source. Whenever one or more annotations were about part of a sentence, we selected the entire sentence, and when an annotation spanned over multiple sentences, we selected each of them.
Ultimately, we ended up with a dataset of four debates, with a total of 5,415 sentences. The agreement between the sources was low as Table TABREF8 shows: only one sentence was selected by all nine sources, 57 sentences by at least five, 197 by at least three, 388 by at least two, and 880 by at least one. The reason for this is that the different media aimed at annotating sentences according to their own editorial line, rather than trying to be exhaustive in any way. This suggests that the task of predicting which sentence would contain check-worthy claims will be challenging. Thus, below we focus on a ranking task rather than on absolute predictions. Moreover, we predict which sentence would be selected (i) by at least one of the media, or (ii) by a specific medium.
Note that the investigative journalists did not select the check-worthy claims in isolation. Our analysis shows that these include claims that were highly disputed during the debate, that were relevant to the topic introduced by the moderator, etc. We will make use of these contextual dependencies below, which is something that was not previously tried in related work.
In particular, we used four transcripts of the 2016 US election: one vice-presidential and three presidential debates.
Answer: One vice-presidential and three presidential debates.
Question: What language do they look at?
Evidence: We evaluate our model on both English and Chinese segmentation. For both languages we used standard datasets for word segmentation and language modeling. For all datasets, we used train, validation and test splits. Since our model assumes a closed character set, we removed validation and test samples which contain characters that do not appear in the training set. In the English corpora, whitespace characters are removed. In Chinese, they are not present to begin with. Refer to Appendix SECREF9 for dataset statistics.
We evaluate our model on both English and Chinese segmentation
Answer: The answers are shown as follows:
* English
* Chinese
Question: If a straight line passes through the points (4,2) and (8,6) then where does it cross the x axis? Explain your answer.
Answer: First, you must calculate the gradient of the line by finding the change in y over the change in x. In this case, between the first point and the second point the y co-ordinate increases by 6-2=4 and the x co-ordinate increases by 8-4=4, so the gradient is 4/4=1. We are looking at a straight line with the equation y=x+c for some value of c, which we can find by plugging in one of our points, for example, 4=2+c, so c must be 2, and the equation of the line is y=x+2. When a line crosses the x axis then it's y co-ordinate is 0. Therefore in this case the line crosses the x axis at x=-2.
Question: What are the two main components in the model of this paper?
Evidence: In this section, we first describe the two datasets used in our experiments: (1) Wikipedia, and (2) arXiv. Then, we report the experimental details and results.
As discussed above, our model has two main components — BILSTM and INCEPTION— which generate textual and visual representations, respectively.
Answer: ILSTM and INCEPTION— which generate textual and visual representations, respectively.
Question: Tell me whether these are primary or secondary colors: Yellow, Blue, Green, Orange, Red, Violet
Answer: Primary Colors: Red, Yellow, Blue
Secondary Colors: Green, Orange, Violet
Question: What is the difference between a sunny side up egg and a scrambled egg?
Answer: In a sunny side up egg, the yolk is intact, whereas in a scrambled egg, the egg is beaten and scrambled.
Question: What are the two settings in their first experiment?
Evidence: Our first experiment tries to answer what happens when more and more languages are added to the model. There are two settings: adding languages in a random order, or adding the most closely related languages first. Cross-entropy plots for these settings are shown in fig:random and fig:swe.
In both cases, the model degrades gracefully (or even improves) for a number of languages, but then degrades linearly (i.e. exponential growth of perplexity) with exponentially increasing number of languages.
For comparison, fig:swesize compares this to the effect of decreasing the number of parameters in the LSTM by successively halving the hidden state size. Here the behavior is similar, but unlike the Swedish model which got somewhat better when closely related languages were added, the increase in cross-entropy is monotone. It would be interesting to investigate how the number of model parameters needs to be scaled up in order to accommodate the additional languages, but unfortunately the computational resources for such an experiment increases with the number of languages and would not be practical to carry out with our current equipment.
Our first experiment tries to answer what happens when more and more languages are added to the model. There are two settings: adding languages in a random order, or adding the most closely related languages first.
Answer: Adding languages in a random order, or adding the most closely related languages first.
Question: Which characters belong to DC or Marvel Universe? Deadpool or Deadstroke
Answer: Deadpool is Marvel, Deathstroke is DC
Question: How many shared layers are in the system?
Evidence: FLOAT SELECTED: Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.
FLOAT SELECTED: Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.
Answer: 1
Question: Why do people like California?
Answer: California is popular for its moderate climate and diverse nature, ranging from. mountainous terrain to sandy beaches and everything in between.
Question: Where is Chiria
Answer: Chiria is a census town in Pashchimi Singhbhum district in the state of Jharkhand, India. India's largest iron ore mine with reserves of 2 billion tonnes of iron ore is located here. The mine is operated by Steel Authority of India Limited.
Question: From the passage provided, extract the number of bags of coffee São Paulo produced in 1888.
Evidence: Coffee production attracted immigrants in search of better economic opportunities in the early 1900s. Mainly, these were Portuguese, Italian, Spanish, German, and Japanese nationals. For instance, São Paulo received approximately 733,000 immigrants in the decade preceding 1900, whilst only receiving approximately 201,000 immigrants in the six years to 1890. The production yield of coffee increases. In 1880, São Paulo produced 1.2 million bags (25% of total production), in 1888 2.6 million (40%), and in 1902 8 million bags (60%). Coffee is then 63% of the country's exports. The gains made by this trade allow sustained economic growth in the country.
Answer: São Paulo produced 2.6 million bags of coffee in 1888.
Question: What parsers do they train for each language in the CoNLL 2009 dataset?
Evidence: The standard approach to multilingual NLP is to design a single architecture, but tune and train a separate model for each language. While this method allows for customizing the model to the particulars of each language and the available data, it also presents a problem when little data is available: extensive language-specific annotation is required. The reality is that most languages have very little annotated data for most NLP tasks.
ammar2016malopa found that using training data from multiple languages annotated with Universal Dependencies BIBREF1 , and represented using multilingual word vectors, outperformed monolingual training. Inspired by this, we apply the idea of training one model on multiple languages—which we call polyglot training—to PropBank-style semantic role labeling (SRL). We train several parsers for each language in the CoNLL 2009 dataset BIBREF0 : a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset. To our knowledge, this is the first multilingual SRL approach to combine supervision from several languages.
The CoNLL 2009 dataset includes seven different languages, allowing study of trends across the same. Unlike the Universal Dependencies dataset, however, the semantic label spaces are entirely language-specific, making our task more challenging. Nonetheless, the success of polyglot training in this setting demonstrates that sharing of statistical strength across languages does not depend on explicit alignment in annotation conventions, and can be done simply through parameter sharing. We show that polyglot training can result in better labeling accuracy than a monolingual parser, especially for low-resource languages. We find that even a simple combination of data is as effective as more complex kinds of polyglot training. We include a breakdown into label categories of the differences between the monolingual and polyglot models. Our findings indicate that polyglot training consistently improves label accuracy for common labels.
We train several parsers for each language in the CoNLL 2009 dataset: a traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset.
Answer: A traditional monolingual version, and variants which additionally incorporate supervision from English portion of the dataset.
Question: In 64th Annual Grammy Awards,  best album of the year award was given to
Answer: Jon Batiste was awarded the best album of the year in 64th Annual Grammy Awards
Question: List some ideas for gifts I could give to my sister on her birthday?
Answer: Chocolate
A book you loved
A cute top
A floppy straw sun hat
Take her out to brunch
Print her out a book of photos of the two of you growing up
Make a box of self-care items like good lotion, a sleep mask, etc.
Give her a board game at a special party you arrange, at which to play it!
Question: How do they obtain language identities?
Evidence: In this paper, we propose an improved RNN-T model with language bias to alleviate the problem. The model is trained to predict language IDs as well as the subwords. To ensure the model can learn CS information, we add language IDs in the CS point of transcription, as illustrated in Fig. 1. In the figure, we use the arrangements of different geometric icons to represent the CS distribution. Compared with normal text, the tagged data can bias the RNN-T to predict language IDs in CS points. So our method can model the CS distribution directly, no additional LID model is needed. Then we constrain the input word embedding with its corresponding language ID, which is beneficial for model to learn the language identity information from transcription. In the inference process, the predicted language IDs are used to adjust the output posteriors. The experiment results on CS corpus show that our proposed method outperforms the RNN-T baseline (without language bias) significantly. Overall, our best model achieves 16.2% and 12.9% relative error reduction on two test sets, respectively. To our best knowledge, this is the first attempt of using the RNN-T model with language bias as an end-to-end CSSR strategy.
In this paper, we propose an improved RNN-T model with language bias to alleviate the problem. The model is trained to predict language IDs as well as the subwords. To ensure the model can learn CS information, we add language IDs in the CS point of transcription, as illustrated in Fig. 1.
Answer: The answers are shown as follows:
* model is trained to predict language IDs as well as the subwords
* we add language IDs in the CS point of transcriptio
Question: Where is the Mall of America located?
Answer: The Mall of America is located in Bloomington, Minnesota, USA.
Question: What is one year?
Answer: One Earth Year is the time it takes for one revolution of the Earth around the sun.
Question: Which of these are contact sports - hockey, badminton, tennis, grid iron football
Answer: hockey and  grid iron football are contact sports
Question: What will the authors explore in the future?
Evidence: In this [id=lq]work, we [id=lq]treat emotion cause extraction as a QA task and propose a new model based on deep memory networks for identifying [id=lq]the emotion causes for an emotion expressed in text. [id=lq]The key property of this approach is the use of context information in the learning process which is ignored in the original memory network. Our new [id=lq]memory network architecture is able [id=lq]to store context in different memory slots to capture context information [id=lq]in proper sequence by convolutional operation. Our model achieves the state-of-the-art performance on a dataset for emotion cause detection when compared to a number of competitive baselines. In the future, we will explore effective ways [id=lq]to model discourse relations among clauses and develop a QA system which can directly output the cause of emotions as answers.
In the future, we will explore effective ways to model discourse relations among clauses and develop a QA system which can directly output the cause of emotions as answers.
Answer: In the future, the authors will explore effective ways to model discourse relations among clauses and develop a QA system which can directly output the cause of emotions as answers.
Question: What are their initial results on this task?
Evidence: FLOAT SELECTED: Table 5: Overall and domain-specific Precision, Recall, and F1 scores for our models. The first row computes similarity directly from the TF-IDF matrix, as in (McCoy et al., 2015). All other rows are classifier outputs.
FLOAT SELECTED: Table 5: Overall and domain-specific Precision, Recall, and F1 scores for our models. The first row computes similarity directly from the TF-IDF matrix, as in (McCoy et al., 2015). All other rows are classifier outputs.
Answer: Achieved the highest per-domain scores on Substance (F1 ≈ 0.8) and the lowest scores on Interpersonal and Mood (F1 ≈ 0.5), and show consistency in per-domain performance rankings between MLP and RBF models.