DataHammer/scimrc huggingface dataset:
Question: How large is the corpus?
Evidence: The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. The corpus comprises 8,275 sentences and 167,739 words in total. However, as mentioned above, only case presentation sections, headings and abstracts are annotated. The numbers of annotated entities are summarized in Table TABREF24.
Findings are the most frequently annotated type of entity. This makes sense given that findings paint a clinical picture of the patient's condition. The number of tokens per entity ranges from one token for all types to 5 tokens for cases (average length 3.1), nine tokens for conditions (average length 2.0), 16 tokens for factors (average length 2.5), 25 tokens for findings (average length 2.6) and 18 tokens for modifiers (average length 1.4) (cf. Table TABREF24). Examples of rather long entities are given in Table TABREF25.
Entities can appear in a discontinuous way. We model this as a relation between two spans which we call “discontinuous” (cf. Figure FIGREF26). Especially findings often appear as discontinuous entities, we found 543 discontinuous finding relations. The numbers for conditions and factors are lower with seven and two, respectively. Entities can also be nested within one another. This happens either when the span of one annotation is completely embedded in the span of another annotation (fully-nested; cf. Figure FIGREF12), or when there is a partial overlapping between the spans of two different entities (partially-nested; cf. Figure FIGREF12). There is a high number of inter-sentential relations in the corpus (cf. Table TABREF27). This can be explained by the fact that the case entity occurs early in each document; furthermore, it is related to finding and factor annotations that are distributed across different sentences.
The most frequently annotated relation in our corpus is the has-relation between a case entity and the findings related to that case. This correlates with the high number of finding entities. The relations contained in our corpus are summarized in Table TABREF27.
The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. The corpus comprises 8,275 sentences and 167,739 words in total.
Answer: The corpus consists of 53 documents, which contain an average number of 156.1 sentences per document, each with 19.55 tokens on average. The corpus comprises 8,275 sentences and 167,739 words in total.
Question: Does the authors' model obtain better performances in the MultiDomain scenario with an average improvement of 4.5%?
Evidence: We compare performances of our model with the implementation of BIBREF13 and the results are shown in Table TABREF43 . Our model obtains better performances in Multi-Domain scenario with an average improvement of 4.5%, where datasets are product reviews on different domains with similar sequence lengths and the same class number, thus producing stronger correlations. Multi-Cardinality scenario also achieves significant improvements of 2.77% on average, where datasets are movie reviews with different cardinalities.
However, Multi-Objective scenario benefits less from multi-task learning due to lacks of salient correlation among sentiment, topic and question type. The QC dataset aims to classify each question into six categories and its performance even gets worse, which may be caused by potential noises introduced by other tasks. In practice, the structure of our model is flexible, as couplings and fusions between some empirically unrelated tasks can be removed to alleviate computation costs.
We further explore the influence of INLINEFORM0 in TOS on our model, which can be any positive integer. A higher value means larger and more various samples combinations, while requires higher computation costs.
Figure FIGREF45 shows the performances of datasets in Multi-Domain scenario with different INLINEFORM0 . Compared to INLINEFORM1 , our model can achieve considerable improvements when INLINEFORM2 as more samples combinations are available. However, there are no more salient gains as INLINEFORM3 gets larger and potential noises from other tasks may lead to performance degradations. For a trade-off between efficiency and effectiveness, we determine INLINEFORM4 as the optimal value for our experiments.
In order to measure the correlation strength between two task INLINEFORM0 and INLINEFORM1 , we learn them jointly with our model and define Pair-wise Performance Gain as INLINEFORM2 , where INLINEFORM3 are the performances of tasks INLINEFORM4 and INLINEFORM5 when learned individually and jointly.
We calculate PPGs for every two tasks in Table TABREF35 and illustrate the results in Figure FIGREF47 , where darkness of colors indicate strength of correlation. It is intuitive that datasets of Multi-Domain scenario obtain relatively higher PPGs with each other as they share similar cardinalities and abundant low-level linguistic characteristics. Sentences of QC dataset are much shorter and convey unique characteristics from other tasks, thus resulting in quite lower PPGs.
Our model obtains better performances in MultiDomain scenario with an average improvement of 4.5%, where datasets are product reviews on different domains with similar sequence lengths and the same class number, thus producing stronger correlations.
Answer: Yes, it does.
Question: What professions are likely to be pursued in Houston?
Evidence: The arrival of the 20th century brought a convergence of economic factors that fueled rapid growth in Houston, including a burgeoning port and railroad industry, the decline of Galveston as Texas's primary port following a devastating 1900 hurricane, the subsequent construction of the Houston Ship Channel, and the Texas oil boom. In the mid-20th century, Houston's economy diversified, as it became home to the Texas Medical Center—the world's largest concentration of healthcare and research institutions—and NASA's Johnson Space Center, home to the Mission Control Center.
Answer: Houston appears to be a good place to be a doctor, aerospace engineer, or researcher. In the past it may have been a hotbed for workers in the oil or transportation industries.
Question: By how much does their system outperform the lexicon-based models?
Evidence: TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline. We use the concatenation of the title and the body as the query to retrieve the candidate comment set by means of the similarity of the tf-idf value. The model is trained on unpaired articles and comments, which is the same as our proposed model.
NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.
Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation. We first compare our proposed model with other popular unsupervised methods, including TF-IDF, LDA, and NVDM. TF-IDF retrieves the comments by similarity of words rather than the semantic meaning, so it achieves low scores on all the retrieval metrics. The neural variational document model is based on the neural VAE framework. It can capture the semantic information, so it has better performance than the TF-IDF model. LDA models the topic information, and captures the deeper relationship between the article and comments, so it achieves improvement in all relevance metrics. Finally, our proposed model outperforms all these unsupervised methods, mainly because the proposed model learns both the semantics and the topic information.
Table TABREF32 shows the performance for our models and the baselines in generative evaluation. Similar to the retrieval evaluation, our proposed model outperforms the other unsupervised methods, which are TF-IDF, NVDM, and LDA, in generative evaluation. Still, the supervised IR achieves better scores than the seq2seq model. With the help of our proposed model, both IR and S2S achieve an improvement under the semi-supervised scenarios.
FLOAT SELECTED: Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)
FLOAT SELECTED: Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.)
TF-IDF (Lexical, Non-Neural) is an important unsupervised baseline.
NVDM (Lexical, Neural) is a VAE-based approach for document modeling BIBREF10 . We compare our model with this baseline to demonstrate the effect of modeling topic.
Table TABREF31 shows the performance of our models and the baselines in retrieval evaluation.
Table TABREF32 shows the performance for our models and the baselines in generative evaluation.
FLOAT SELECTED: Table 2: The performance of the unsupervised models and supervised models under the retrieval evaluation settings. (Recall@k, MRR: higher is better; MR: lower is better.)
FLOAT SELECTED: Table 3: The performance of the unsupervised models and supervised models under the generative evaluation settings. (METEOR, ROUGE, CIDEr, BLEU: higher is better.)
Answer: Proposed model is better than both lexical based models by significan margin in all metrics: BLEU 0.261 vs 0.250, ROUGLE 0.162 vs 0.155 etc.
Question: How many continents are there in total?
Answer: There are 7 continents in total.
Question: What rank did Elvis Presley hold in the US Army?
Evidence: Elvis Aaron Presley (January 8, 1935 – August 16, 1977), often referred to mononymously as Elvis, was an American singer, actor and sergeant in the United States Army. Dubbed the "King of Rock and Roll", he is regarded as one of the most significant cultural figures of the 20th century. His energized interpretations of songs and sexually provocative performance style, combined with a singularly potent mix of influences across color lines during a transformative era in race relations, led him to both great success and initial controversy.
Answer: Elvis Presley held the rank of sergeant in the United States Army.
Question: What are the 19 fine-grained semantic classes?
Evidence: We describe a dataset developed for Named Entity Recognition in German federal court decisions. It consists of approx. 67,000 sentences with over 2 million tokens. The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person, judge, lawyer, country, city, street, landscape, organization, company, institution, court, brand, law, ordinance, European legal norm, regulation, contract, court decision, and legal literature. The legal documents were, furthermore, automatically annotated with more than 35,000 TimeML-based time expressions. The dataset, which is available under a CC-BY 4.0 license in the CoNNL-2002 format, was developed for training an NER service for German legal documents in the EU project Lynx.
The resource contains 54,000 manually annotated entities, mapped to 19 fine-grained semantic classes: person , judge , lawyer , country , city , street , landscape , organization , company , institution , court , brand , law , ordinance , European legal norm , regulation , contract , court decision, and legal literature.
Answer: Person , judge , lawyer , country , city , street , landscape , organization , company , institution , court , brand , law , ordinance , European legal norm , regulation , contract , court decision, and legal literature.
Question: How do you prove model identifiability?
Evidence: Zhang and proposed the post-nonlinear (PNL) causal model taking account of real-world data generating processes. In the PNL model, variables are generated as follows:
where X pa i is a set of direct cause variables, or parents, of x i and e i is an unobserved noise variable.
The inner function f i : R |Xpa i | → R represents nonlinear mixture of the causes and the outer one g i : R → R represents nonlinear distortion of sensors that often observed in real-world situations. The noises are independent of each other, e i ⊥ e j (∀i ̸ = j), and thus, a noise is independent of each of the corresponding causes, e i ⊥ x j ∈ X pa i . The distortion g i is assumed to be invertible. The PNL model is identifiable except for some special combinations of the functions and the noise distributions. The PNL model is known as the most general identifiable causal model and is equivalent to LiNGAM when f is linear and g is the identity and ANM when g is the identity.
The PNL model is identifiable except for some special combinations of the functions and the noise
distributions (Zhang and Hyvarinen ¨ , 2009; Peters et al., 2011, 2014). The PNL model is known as
the most general identifiable causal model and is equivalent to LiNGAM when f is linear and g is
the identity and ANM when g is the identity
Answer: The identifiability of the multivariate PNL model is already shown in the existing studies as stated in Section 2.2. We focused on the development of its estimation method in this paper.
Question: classify the following items as income or spending: salary, movie, tax return, car rent, food
Answer: salary is income, car rent is income, movie is spending, car rent is spending, food is spending
Question: What is linear programming and how to solve?
Answer: Linear programming is a method to achieve the best objective in an optimization problem whose objective function and requirements are represented by linear relationships. The simplex method and the interior-point method are common algorithms to solve linear programming problems.

You can visit https://en.wikipedia.org/wiki/Linear_programming to learn more.
Question: What year did the hindenburg disaster occur?
Answer: The Hindenburg disaster occurred on May 6, 1937, when the Hindenburg airship caught fire and was destroyed during its attempt to dock with its mooring mast at Naval Air Station Lakehurst.
Question: Which language directions are machine translation systems of WMT evaluated on?
Evidence: We participated in the WMT19 shared news translation task in 11 translation directions. We achieved first place for 8 directions: German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English, and three other directions were placed second (ranked by teams), which included Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh.
We achieved first place for 8 directions: German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English, and three other directions were placed second (ranked by teams), which included Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh.
Answer: The answers are shown as follows:
* German$\leftrightarrow $English, German$\leftrightarrow $French, Chinese$\leftrightarrow $English, English$\rightarrow $Lithuanian, English$\rightarrow $Finnish, and Russian$\rightarrow $English
* Lithuanian$\rightarrow $English, Finnish$\rightarrow $English, and English$\rightarrow $Kazakh
Question: What are the different ways in which a batsman in cricket can be dismissed
Answer: There are 11 ways of getting out or dismissed in cricket: caught, leg before wicket (LBW), stumped, hit wicket, run out, bowled, handled the ball, obstructing the field, stumped, hitting the ball twice, and retired.
Question: What would you consider when preparing a Dj set?
Answer: There are many aspects to consider.  Initially having an understanding of the audience and setting, can help in thinking about energy levels (BPMs), and duration.

It is also helpful to plan the direction of the DJ set, ramping from slow BPMs to high ones, perhaps peaking mid-set or just a different series of peaks along the way.

Track selection is key. Having a pre-defined list of potential tracks to play is a great idea.

Another consideration is mixing in key or following a harmonic mixing style for a neat flow.

All of the above require preparation and programming for a well-crafted DJ mix.
Question: Who is the first astronaut to get into space in China?
Evidence: In 2003, Yang Liwei was launched aboard Shenzhou 5, becoming the first person sent into space by the Chinese space program. This achievement made China the third country to independently send humans into space.
Answer: Yang Liwei, who reached space in 2003 onboard the Shenzhou 5 space ship, is the first astronaut in China to reach space.
Question: What do the authors introduce in the paper?
Evidence: The task of interpreting and following natural language (NL) navigation instructions involves interleaving different signals, at the very least the linguistic utterance and the representation of the world. For example, in turn right on the first intersection, the instruction needs to be interpreted, and a specific object in the world (the intersection) needs to be located in order to execute the instruction. In NL navigation studies, the representation of the world may be provided via visual sensors BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4 or as a symbolic world representation. This work focuses on navigation based on a symbolic world representation (referred to as a map).
Previous datasets for NL navigation based on a symbolic world representation, HCRC BIBREF5, BIBREF6, BIBREF7 and SAIL BIBREF8, BIBREF9, BIBREF10, BIBREF11, BIBREF12, BIBREF13, BIBREF14, BIBREF15 present relatively simple worlds, with a small fixed set of entities known to the navigator in advance. Such representations bypass the great complexity of real urban navigation, which consists of long paths and an abundance of previously unseen entities of different types.
In this work we introduce Realistic Urban Navigation (RUN), where we aim to interpret navigation instructions relative to a rich symbolic representation of the world, given by a real dense urban map. To address RUN, we designed and collected a new dataset based on OpenStreetMap, in which we align NL instructions to their corresponding routes. Using Amazon Mechanical Turk, we collected 2515 instructions over 3 regions of Manhattan, all specified (and verified) by (respective) sets of humans workers. This task raises several challenges. First of all, we assume a large world, providing long routes, vulnerable to error propagation; secondly, we assume a rich environment, with entities of various different types, most of which are unseen during training and are not known in advance; finally, we evaluate on the full route intended, rather than on last-position only.
We then propose a strong neural baseline for RUN where we augment a standard encoder-decoder architecture with an entity abstraction layer, attention over words and worlds, and a constantly updating world-state. Our experimental results and ablation study show that this architecture is indeed better-equipped to treat grounding in realistic urban settings than standard sequence-to-sequence architectures. Given this RUN benchmark, empirical results, and evaluation procedure, we hope to encourage further investigation into the topic of interpreting NL instructions in realistic and previously unseen urban domains.
[1]The task defined by BIBREF6 is of moving between entities only.
In this work we introduce Realistic Urban Navigation (RUN), where we aim to interpret navigation instructions relative to a rich symbolic representation of the world, given by a real dense urban map.
Answer: Realistic Urban Navigation (RUN).
Question: Which of the following are beach wear vs what one would wear on a cold winter trek? If there are common items, create a third category called 'Common':

Socks
Hats
Neck gaiter
Sunglasses
Swimsuits
Undershirts
Cover-ups
Down jacket
Trek poles
Water Shoes
Beanie
Sandals
Answer: Beach wear:
Swimsuits
Cover-ups
Sandals
Water Shoes

Cold winter trek wear:
Neck gaiter
Down jacket
Beanie
Trek poles

Common:
Socks
Hats
Sunglasses
Undershirts
Question: What does Thai Songkran festival represents and how many days does it last?
Evidence: Songkran is a term derived from Sanskrit संक्रान्ति saṅkrānti meaning 'to move' or 'movement'. It derives from the movement of the sun from one position to another in the zodiac. According to its literal meaning in Sanskrit, a Songkran occurs every month. However, the period that Thai people refer to as Songkran happens when the sun moves from Pisces to Aries in the zodiac. The correct name for this period should actually be Maha Songkran ('great Songkran) because it coincides with the arrival of a New Year. The Songkran festival is, therefore, a celebration of the New Year in accordance with the solar calendar. The celebration covers a period of three days: 13 April is regarded as Maha Songkran, the day that the sun moves into Aries on the zodiac or the last day of the old year. The next day, 14 April is called Wan Nao, the transitional day between the old and the new years, and 15 April is called Wan Thaloeng Sok (Thai: วันเถลิงศก 'to begin a new era or year'), New Year's day itself.
Answer: Thai Songkran festival is a New Year celebration in Thailand according to solar calendar. Songkran happens when the sun moves from Pisces to Aries in the zodiac. The festival covers three days period from 13 April to 15 April.
Question: What is their dataset?
Evidence: The YummlyBIBREF8 dataset is used to understand how ingredients can be used to determine the cuisine. The dataset consists of 39,774 recipes. Each recipe is associated with a particular cuisine and a particular set of ingredients. Initial analysis of the data-set revealed a total of 20 different cuisines and 6714 different ingredients. Italian cuisine, with 7383 recipes, overshadows the dataset.
The numbers of recipes for the 19 cuisines is quite imbalanced.BIBREF9 The following graph shows the count of recipes per cuisine.
User specific data is collected from social media platforms such as Facebook and Instagram with the users permission. These images are then undergo a series of pre processing tasks. This helps in cleaning the data.
The Yummly dataset is used to understand how ingredients can be used to determine the cuisine. The dataset consists of 39,774 recipes. Each recipe is associated with a particular cuisine and a particular set of ingredients. Initial analysis of the data-set revealed a total of 20 different cuisines and 6714 different ingredients.
Answer: The Yummly dataset.