DataHammer/scimrc huggingface dataset:
Question: Who is the greatest cricketer of all time.
Answer: There are many cricketers who could lay claim to the mantle of greatest cricketer of all time. Cricket is a bat and ball game that has been played internationally for nearly 150 years. Two names are often suggested to be the greatest. Sir Donald Bradman and Sir Gary Sobers. Bradman for having a batting average that will never be beaten and Sobers  for being an all rounder who was outstanding in bowling batting and fielding
Both of these players would be picked in any best test team of all time.  The only caveat is that neither player excelled or played in the shorter forms of the game.
Question: Given a reference text about Philip, Duke of Parma, who were his parents?
Evidence: Philip, Duke of Parma

Philip (Spanish: Felipe, Italian: Filippo; 15 March 1720 – 18 July 1765) was Duke of Parma from 18 October 1748 until his death in 1765. He was born a Spanish infante, being the second son of King Philip V and Elisabeth Farnese. The Duchy of Parma had been ruled by the House of Farnese, Queen Elisabeth's family. Philip founded the House of Bourbon-Parma, a cadet line of the House of Bourbon. He was a first cousin and son-in-law of the French king Louis XV.
Answer: Philip, Duke of Parma's parents were King Philip V and Elisabeth Farnese.
Question: List five titles by Mary Shelley.
Answer: Frankenstein, The Last Man, Lodore, The Mortal Immortal, Maurice
Question: What quality does this task investigate?
Evidence: Current societies are exposed to a continuous flow of information that results in a large production of data (e.g. news articles, micro-blogs, social media posts, among others), at different moments in time. In addition to this, the consumption of information has dramatically changed: more and more people directly access information through social media platforms (e.g. Facebook and Twitter), and are less and less exposed to a diversity of perspectives and opinions. The combination of these factors may easily result in information overload and impenetrable “filter bubbles”. Events, i.e. things that happen or hold as true in the world, are the basic components of such data stream. Being able to correctly identify and classify them plays a major role to develop robust solutions to deal with the current stream of data (e.g. the storyline framework BIBREF0 ), as well to improve the performance of many Natural Language Processing (NLP) applications such as automatic summarization and question answering (Q.A.).
Event detection and classification has seen a growing interest in the NLP community thanks to the availability of annotated corpora BIBREF1 , BIBREF2 , BIBREF3 , BIBREF4 and evaluation campaigns BIBREF5 , BIBREF6 , BIBREF7 , BIBREF8 , BIBREF9 , BIBREF10 . In the context of the 2014 EVALITA Workshop, the EVENTI evaluation exercise BIBREF11 was organized to promote research in Italian Temporal Processing, of which event detection and classification is a core subtask.
Since the EVENTI campaign, there has been a lack of further research, especially in the application of deep learning models to this task in Italian. The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier. The pre-trained models and scripts running the system (or re-train it) are publicly available. .
The contributions of this paper are the followings: i.) the adaptation of a state-of-the-art sequence to sequence (seq2seq) neural system to event detection and classification for Italian in a single step approach; ii.) an investigation on the quality of existing Italian word embeddings for this task; iii.) a comparison against a state-of-the-art discrete classifier.
Answer: It investigates the quality of existing Italian word embeddings.
Question: Where did the authors come from for their experimental evaluation?
Evidence: The datasets we use for our experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history. The generated data represents one of the contributions of our paper. The datasets are the following:
Entity Classes. We focus on a manually predetermined set of entity classes for which we expect to have news coverage. The number of analyzed entity classes is 27, including INLINEFORM0 entities with at least one news reference. The entity classes were selected from the DBpedia class ontology. Figure FIGREF42 shows the number of entities per class for the years (2009-2014).
News Articles. We extract all news references from the collected Wikipedia entity pages. The extracted news references are associated with the sections in which they appear. In total there were INLINEFORM0 news references, and after crawling we end up with INLINEFORM1 successfully crawled news articles. The details of the news article distribution, and the number of entities and sections from which they are referred are shown in Table TABREF44 .
Article-Entity Ground-truth. The dataset comprises of the news and entity pairs INLINEFORM0 . News-entity pairs are relevant if the news article is referenced in the entity page. Non-relevant pairs (i.e. negative training examples) consist of news articles that contain an entity but are not referenced in that entity's page. If a news article INLINEFORM1 is referred from INLINEFORM2 at year INLINEFORM3 , the features are computed taking into account the entity profiles at year INLINEFORM4 .
Article-Section Ground-truth. The dataset consists of the triple INLINEFORM0 , where INLINEFORM1 , where we assume that INLINEFORM2 has already been determined as relevant. We therefore have a multi-class classification problem where we need to determine the section of INLINEFORM3 where INLINEFORM4 is cited. Similar to the article-entity ground truth, here too the features compute the similarity between INLINEFORM5 , INLINEFORM6 and INLINEFORM7 .
The datasets we use for our experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history.
Answer: The datasets they use for their experimental evaluation are directly extracted from the Wikipedia entity pages and their revision history.
Question: per this reference text about Delaware Route 16, how many times does the road change between being a divided highway and undivided?
Evidence: In Saint Johnstown, the road becomes a divided highway and DE 36 splits from DE 16 by heading to the northeast on Shawnee Road. DE 16 soon becomes undivided again and continues east through agricultural areas and woods with some residences, crossing Toms Dam Branch and running through the community of Owens. Farther east, the road passes through the community of Oakley and crosses West Branch Gum Branch and then Gum Branch. The route comes to an intersection with US 113 near a few businesses, where it is briefly a divided highway. Past this intersection, the name changes to Milton Ellendale Highway and the two-lane undivided road enters the town of Ellendale, where it becomes Main Street. In Ellendale, DE 16 runs past several homes, crossing the Delmarva Central Railroad's Indian River Subdivision line at-grade. The road leaves the town and becomes Milton Ellendale Highway again, turning southeast. The route heads through woodland and farms, running immediately to the north of the Delmarva Central Railroad's Milton Industrial Track line. DE 16 turns east away from the railroad tracks and comes to an intersection with DE 30. At this point, DE 5 Alternate (DE 5 Alt.), which runs south along DE 30, heads east along with DE 16. The road reaches the town of Milton, where it passes homes and businesses. DE 16 comes to an intersection with DE 5, where DE 5 Alt. ends and the road name changes to Broadkill Road. The route leaves Milton and continues into farmland with a few residences, turning to the northeast. The road intersects DE 1 and passes north of a golf course before it runs through a mix of farms and woods with some homes. DE 16 turns east and northeast through marshland within the Prime Hook National Wildlife Refuge before crossing Broadkill Sound and reaching the community of Broadkill Beach. Here, the route intersects Bay Shore Drive before ending at a dead end on the Delaware Bay.
Answer: DE Route 16 changes 3 times between divided and undivided highways per the information provided.
Question: How many datasets do the authors conduct extensive experiments on?
Evidence: Neural network based models have been widely exploited with the prosperities of Deep Learning BIBREF0 and achieved inspiring performances on many NLP tasks, such as text classification BIBREF1 , BIBREF2 , semantic matching BIBREF3 , BIBREF4 and machine translation BIBREF5 . These models are robust at feature engineering and can represent words, sentences and documents as fix-length vectors, which contain rich semantic information and are ideal for subsequent NLP tasks.
One formidable constraint of deep neural networks (DNN) is their strong reliance on large amounts of annotated corpus due to substantial parameters to train. A DNN trained on limited data is prone to overfitting and incapable to generalize well. However, constructions of large-scale high-quality labeled datasets are extremely labor-intensive. To solve the problem, these models usually employ a pre-trained lookup table, also known as Word Embedding BIBREF6 , to map words into vectors with semantic implications. However, this method just introduces extra knowledge and does not directly optimize the targeted task. The problem of insufficient annotated resources is not solved either.
Multi-task learning leverages potential correlations among related tasks to extract common features, increase corpus size implicitly and yield classification improvements. Inspired by BIBREF7 , there are a large literature dedicated for multi-task learning with neural network based models BIBREF8 , BIBREF9 , BIBREF10 , BIBREF11 . These models basically share some lower layers to capture common features and further feed them to subsequent task-specific layers, which can be classified into three types:
In this paper, we propose a generalized multi-task learning architecture with four types of recurrent neural layers for text classification. The architecture focuses on Type-III, which involves more complicated interactions but has not been researched yet. All the related tasks are jointly integrated into a single system and samples from different tasks are trained in parallel. In our model, every two tasks can directly interact with each other and selectively absorb useful information, or communicate indirectly via a shared intermediate layer. We also design a global memory storage to share common features and collect interactions among all tasks.
We conduct extensive experiments on five benchmark datasets for text classification. Compared to learning separately, jointly learning multiple relative tasks in our model demonstrate significant performance gains for each task.
Our contributions are three-folds:
We conduct extensive experiments on five benchmark datasets for text classification.
Answer: Five.
Question: What's the capital of Brazil?
Answer: The capital of Brazil is Brasilia, where the Federal government is located as well. The city become the capital in 1960, as a fully planned city and contemplates a modern architecture designed by Oscar Niemeyer.
Question: Which of these were summer olympic sports in 2020: tennis, cheerleading, chess, boxing, basketball, ski jumping
Answer: Tennis, boxing, basketball
Question: How do clouds form?
Answer: Clouds form when warm air and water vapor rise. They rise to a point where the air is cool enough for the water vapor to condense into water droplets. Water droplets that are completely saturated with water vapor, but unable to hold all of it as vapor, begin to condense into liquid/solid forms.
Question: What are the author's evaluation indicators for the model?
Evidence: Each model produces four scores and each score is the probability that a tweet includes harassment language, indirect, physical and sexual harassment language respectively. For any tweet, we first check the score of the harassment language and if it is less than a specified threshold, then the harassment label is zero, so the other three labels are zero as well. If it is greater than or equal to that threshold, then the harassment label is one and the type of harassment is the one among these three having that has the greatest score (highest probability). We set this threshold equal to 0.33.
We compare eight different models in our experiments. Four of them have a Projected Layer (see Fig. FIGREF2), while the others do not have, and this is the only difference between these two groups of our models. So, we actually include four models in our experiments (having a projected layer or not). Firstly, LastStateRNN is the classic RNN model, where the last state passes through an MLP and then the LR Layer estimates the corresponding probability. In contrast, in the AvgRNN model we consider the average vector of all states that come out of the cells. The AttentionRNN model is the one that it has been presented in BIBREF9. Moreover, we introduce the MultiAttentionRNN model for the harassment language detection, which instead of one attention, it includes four attentions, one for each category.
We have evaluated our models considering the F1 Score, which is the harmonic mean of precision and recall. We have run ten times the experiment for each model and considered the average F1 Score. The results are mentioned in Table TABREF11. Considering F1 Macro the models that include the multi-attention mechanism outperform the others and particularly the one with the Projected Layer has the highest performance. In three out of four pairs of models, the ones with the Projected Layer achieved better performance, so in most cases the addition of the Projected Layer had a significant enhancement.
For any tweet, we first check the score of the harassment language and if it is less than a specified threshold, then the harassment label is zero, so the other three labels are zero as well. If it is greater than or equal to that threshold, then the harassment label is one and the type of harassment is the one among these three having that has the greatest score (highest probability). We set this threshold equal to 0.33.
Answer: For any tweet, they first check the score of the harassment language and if it is less than a specified threshold, then the harassment label is zero, so the other three labels are zero as well. If it is greater than or equal to that threshold, then the harassment label is one and the type of harassment is the one among these three having that has the greatest score.
Question: Based on the reference text about Bitcoin, how many countries have banned Bitcoin?
Evidence: Bitcoin (abbreviation: BTC or XBT; sign: ₿) is a protocol which implements a highly available, public, permanent, and decentralized ledger. In order to add to the ledger, a user must prove they control an entry in the ledger. The protocol specifies that the entry indicates an amount of a token, bitcoin with a minuscule b. The user can update the ledger, assigning some of their bitcoin to another entry in the ledger. Because the token has characteristics of money, it can be thought of as a digital currency.

Bitcoin transactions are verified by network nodes through cryptography and recorded in a public distributed ledger called a blockchain. The cryptocurrency was invented in 2008 by an unknown person or group of people using the name Satoshi Nakamoto. The currency began use in 2009, when its implementation was released as open-source software.: ch. 1  The word "bitcoin" was defined in a white paper published on October 31, 2008. It is a compound of the words bit and coin.

The Library of Congress reports that, as of November 2021, nine countries have fully banned bitcoin use, while a further forty-two have implicitly banned it. A few governments have used bitcoin in some capacity. El Salvador has adopted Bitcoin as legal tender, although use by merchants remains low. Ukraine has accepted cryptocurrency donations to fund the resistance to the 2022 Russian invasion. Iran has used bitcoin to bypass sanctions.
Answer: As of November 2021, 42 countries have implicitly banned and 9 countries have fully banned the use of Bitcoin.
Question: Was the structure of regulatory filings exploited when training the model? 
Evidence: We extend BERT Base-Chinese (12-layer, 768-hidden, 12-heads, 110M parameters) for sequence labeling. All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end. All inputs are then padded to a length of 256 tokens. After feeding through BERT, we obtain the hidden state of the final layer, denoted as ($h_{1}$, $h_{2}$, ... $h_{N}$) where $N$ is the max length setting. We add a fully-connected layer and softmax on top, and the final prediction is formulated as:
All documents are segmented into paragraphs and processed at the paragraph level (both training and inference); this is acceptable because we observe that most paragraphs are less than 200 characters. The input sequences are segmented by the BERT tokenizer, with the special [CLS] token inserted at the beginning and the special [SEP] token added at the end.
Answer: No.
Question: How do you balance efficiency and accuracy？
Evidence: Suppose a user wants to write a sentence “I will be 10 minutes late.” Ideally, she would type just a few keywords such as “10 minutes late” and an autocomplete system would be able to infer the intended sentence (Figure FIGREF1). Existing left-to-right autocomplete systems BIBREF0, BIBREF1 can often be inefficient, as the prefix of a sentence (e.g. “I will be”) fails to capture the core meaning of the sentence. Besides the practical goal of building a better autocomplete system, we are interested in exploring the tradeoffs inherent to such communication schemes between the efficiency of typing keywords, accuracy of reconstruction, and interpretability of keywords.
One approach to learn such schemes is to collect a supervised dataset of keywords-sentence pairs as a training set, but (i) it would be expensive to collect such data from users, and (ii) a static dataset would not capture a real user's natural predilection to adapt to the system BIBREF2. Another approach is to avoid supervision and jointly learn a user-system communication scheme to directly optimize the combination of efficiency and accuracy. However, learning in this way can lead to communication schemes that are uninterpretable to humans BIBREF3, BIBREF4 (see Appendix for additional related work).
In this work, we propose a simple, unsupervised approach to an autocomplete system that is efficient, accurate, and interpretable. For interpretability, we restrict keywords to be subsequences of their source sentences based on the intuition that humans can infer most of the original meaning from a few keywords. We then apply multi-objective optimization approaches to directly control and achieve desirable tradeoffs between efficiency and accuracy.
We observe that naively optimizing a linear combination of efficiency and accuracy terms is unstable and leads to suboptimal schemes. Thus, we propose a new objective which optimizes for communication efficiency under an accuracy constraint. We show this new objective is more stable and efficient than the linear objective at all accuracy levels.
As a proof-of-concept, we build an autocomplete system within this framework which allows a user to write sentences by specifying keywords. We empirically show that our framework produces communication schemes that are 52.16% more accurate than rule-based baselines when specifying 77.37% of sentences, and 11.73% more accurate than a naive, weighted optimization approach when specifying 53.38% of sentences. Finally, we demonstrate that humans can easily adapt to the keyword-based autocomplete system and save nearly 50% of time compared to typing a full sentence in our user study.
We then apply multi-objective optimization approaches to directly control and achieve desirable tradeoffs between efficiency and accuracy.
Answer: They apply multi-objective optimization approaches to directly control and achieve desirable tradeoffs between efficiency and accuracy.
Question: Under what agreement is Macaw distributed？
Evidence: Conversational information seeking (CIS) has been recognized as a major emerging research area in information retrieval. Such research will require data and tools, to allow the implementation and study of conversational systems. This paper introduces Macaw, an open-source framework with a modular architecture for CIS research. Macaw supports multi-turn, multi-modal, and mixed-initiative interactions, and enables research for tasks such as document retrieval, question answering, recommendation, and structured data exploration. It has a modular design to encourage the study of new CIS algorithms, which can be evaluated in batch mode. It can also integrate with a user interface, which allows user studies and data collection in an interactive mode, where the back end can be fully algorithmic or a wizard of oz setup. Macaw is distributed under the MIT License.
Macaw is distributed under the MIT License.
Answer: Macaw is distributed under the MIT License.
Question: What baselines are used for evaluation?
Evidence: The generator performance was assessed on the two evaluation metrics: the BLEU and the slot error rate ERR by adopting code from an open source benchmark toolkit for Natural Language Generation. We compared the proposed models against three strong baselines which have been recently published as state-of-the-art NLG benchmarks[]. https://github.com/shawnwun/RNNLG
HLSTM proposed by BIBREF3 thwsjy15 which used a heuristic gate to ensure that all of the slot-value information was accurately captured when generating.
SCLSTM proposed by BIBREF4 wensclstm15 which can jointly learn the gating signal and language model.
Enc-Dec proposed by BIBREF8 wentoward which applied the attention-based encoder-decoder architecture.
We compared the proposed models against three strong baselines which have been recently published as state-of-the-art NLG bench_x0002_marks5.  • HLSTM proposed by Wen et al. (2015a) which used a heuristic gate to ensure that all of the slot-value information was accurately captured when generating. • SCLSTM proposed by Wen et al. (2015b) which can jointly learn the gating signal and language model. • Enc-Dec proposed by Wen et al. (2016b) which applied the attention-based encoderdecoder architecture.
Answer: HLSTM proposed by Wen et al. (2015a) ,SCLSTM proposed by Wen et al. (2015b) , and  Enc-Dec proposed by Wen et al. (2016b).