DataHammer/scimrc huggingface dataset:
Question: Are the datasets used to evaluate monolingual SRI labeled?
Evidence: We propose a Bayesian model of semantic role induction (SRI) that uses crosslingual latent variables to capture role alignments in parallel corpora. The crosslingual latent variables capture correlations between roles in different languages, and regularize the parameter estimates of the monolingual models. Because this is a joint Bayesian model of multilingual SRI, we can apply the same model to a variety of training scenarios just by changing the inference procedure appropriately. We evaluate monolingual SRI with a large unlabeled dataset, bilingual SRI with a parallel corpus, bilingual SRI with annotations available for the source language, and monolingual SRI with a small labeled dataset. Increasing the amount of monolingual unlabeled data significantly improves SRI in German but not in English. Adding word alignments in parallel sentences results in small, non significant improvements, even if there is some labeled data available in the source language. This difficulty in showing the usefulness of parallel corpora for SRI may be due to the current assumptions about role alignments, which mean that only a small percentage of roles are aligned. Further analyses reveals that annotating small amounts of data can easily outperform the performance gains obtained by adding large unlabeled dataset as well as adding parallel corpora.
Future work includes training on different language pairs, on more than two languages, and with more inclusive models of role alignment.
We evaluate monolingual SRI with a large unlabeled dataset, bilingual SRI with a parallel corpus, bilingual SRI with annotations available for the source language, and monolingual SRI with a small labeled dataset.
Answer: No.
Question: Name the top 10 Sci Fi books of all time?
Answer: Dune by Frank Herbert, 1965; The Left Hand of Darkness by Ursula K. Le Guin, 1969; Foundation Series by Isaac Asimov, 1942; The Hyperion Cantos by Dan Simmons, 1989; The Three Body Problem by Liu Cixin, 2008; The War of the Worlds by H. G. Wells, 1898; Snow Crash by Neal Stephenson, 1992; Fahrenheit 451 by Ray Bradbury, 1953; 2001: A Space Odyssey by Arthur C. Clarke, 1968; Stranger in a Strange Land by Robert A. Heinlein, 1961
Question: How do you decide what type of bicycle to buy?
Answer: To figure out what bicycle to buy, you must determine what type of biking you want to do. The most popular bikes tend to be road, mountain and commuter bikes. Each serves a different purpose, and your shopping decisions should be based on what type of biking you intend to do.
Question: What do they analyze the implications of Wikidata and static knowledge bases extracted from Wikipediaanalyze for?
Evidence: Named Entity Linking is the task of detecting mentions of entities from a knowledge base in free text, as illustrated in Figure 1 .
Most of the entity linking literature focuses on target knowledge bases which are derived from Wikipedia, such as DBpedia BIBREF0 or YAGO BIBREF1 . These bases are curated automatically by harvesting information from the info-boxes and categories on each Wikipedia page and are therefore not editable directly.
Wikidata BIBREF2 is an editable, multilingual knowledge base which has recently gained popularity as a target database for entity linking BIBREF3 , BIBREF4 , BIBREF5 , BIBREF6 . As these new approaches to entity linking also introduce novel learning methods, it is hard to tell apart the benefits that come from the new models and those which come from the choice of knowledge graph and the quality of its data.
We review the main differences between Wikidata and static knowledge bases extracted from Wikipedia, and analyze their implactions for entity linking. We illustrate these differences by building a simple entity linker, OpenTapioca, which only uses data from Wikidata, and show that it is competitive with other systems with access to larger data sources for some tasks. OpenTapioca can be trained easily from a Wikidata dump only, and can be efficiently kept up to date in real time as Wikidata evolves. We also propose tools to adapt existing entity linking datasets to Wikidata, and offer a new entity linking dataset, consisting of affiliation strings extracted from research articles.
We review the main differences between Wikidata and static knowledge bases extracted from Wikipedia, and analyze their implactions for entity linking.
Answer: Entity linking.
Question: What type of music do U2 make?
Evidence: U2 are an Irish rock band from Dublin, formed in 1976. The group consists of Bono (lead vocals and rhythm guitar), the Edge (lead guitar, keyboards, and backing vocals), Adam Clayton (bass guitar), and Larry Mullen Jr. (drums and percussion). Initially rooted in post-punk, U2's musical style has evolved throughout their career, yet has maintained an anthemic quality built on Bono's expressive vocals and the Edge's chiming, effects-based guitar sounds. Bono's lyrics, often embellished with spiritual imagery, focus on personal and sociopolitical themes. Popular for their live performances, the group have staged several ambitious and elaborate tours over their career.
Answer: U2 are a rock band. They started out as a post punk band and although this has evolved they have kept their anthemic style. Their sound is built on Bono's vocals and Edge's guitar. Their lyrics include spiritual images, and themes of a personal and political nature.
Question: What does Neolithic mean?
Answer: "Neolithic" comes from the Greek word for "new stone," and it started about 10,000 years after the Upper Paleolithic. During the Neolithic, people learned how to farm. Animals that were tamed and kept as pets also gave rise to livestock. In the later stages of the Neolithic, copper, bronze, and finally iron were used to make tools. This is what is known as the "Iron Age."
Question: Do the experiments explore how various architectures and layers contribute towards certain decisions?
Evidence: As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $
Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.
As a document classifier we employ a word-based CNN similar to Kim consisting of the following sequence of layers: $ \texttt {Conv} \xrightarrow{} \texttt {ReLU} \xrightarrow{} \texttt {1-Max-Pool} \xrightarrow{} \texttt {FC} \\ $
Future work would include applying LRP to other neural network architectures (e.g. character-based or recurrent models) on further NLP tasks, as well as exploring how relevance information could be taken into account to improve the classifier's training procedure or prediction performance.
Answer: No.
Question: What games are used to test author's methods?
Evidence: TextWorld uses a grammar to generate similar games. Following BIBREF7, we use TextWorld's “home” theme to generate the games for the question-answering system. TextWorld is a framework that uses a grammar to randomly generate game worlds and quests. This framework also gives us information such as instructions on how to finish the quest, and a list of actions that can be performed at each step based on the current world state. We do not let our agent access this additional solution information or admissible actions list. Given the relatively small quest length for TextWorld games—games can be completed in as little as 5 steps—we generate 50 such games and partition them into train and test sets in a 4:1 ratio. The traces are generated on the training set, and the question-answering system is evaluated on the test set.
We choose the game, 9:05 as our target task game due to similarities in structure in addition to the vocabulary overlap. Note that there are multiple possible endings to this game and we pick the simplest one for the purpose of training our agent.
For the horror domain, we choose Lurking Horror to train the question-answering system on. The source and target task games are chosen as Afflicted and Anchorhead respectively. However, due to the size and complexity of these two games some modifications to the games are required for the agent to be able to effectively solve them. We partition each of these games and make them smaller by reducing the final goal of the game to an intermediate checkpoint leading to it. This checkpoints were identified manually using walkthroughs of the game; each game has a natural intermediate goal. For example, Anchorhead is segmented into 3 chapters in the form of objectives spread across 3 days, of which we use only the first chapter. The exact details of the games after partitioning is described in Table TABREF7. For Lurking Horror, we report numbers relevant for the oracle walkthrough. We then pre-prune the action space and use only the actions that are relevant for the sections of the game that we have partitioned out. The majority of the environment is still available for the agent to explore but the game ends upon completion of the chosen intermediate checkpoint.
Given the relatively small quest length for TextWorld games—games can be completed in as little as 5 steps—we generate 50 such games and partition them into train and test sets in a 4:1 ratio.
We choose the game, 9:05 as our target task game due to similarities in structure in addition to the vocabulary overlap. 
For the horror domain, we choose Lurking Horror to train the question-answering system on. The source and target task games are chosen as Afflicted and Anchorhead respectively.
Answer: The answers are shown as follows:
* Lurking Horror
* Afflicted
* Anchorhead
* 9:05
* TextWorld games
Question: Why did they designate Italian as a test-only language?
Evidence: We held out the topics “Healthcare” and “Political System” from the training data and created a separate cross-topic test set that contains the questions and answers related to those topics.
Furthermore, in order to test cross-question generalization performance within previously seen topics, we manually selected 16 held-out questions that are distributed over the remaining 10 topics. We selected the held-out questions manually because we wanted to make sure that they are truly unseen and that no paraphrases of the questions are found in the training set.
We designated Italian as a test-only language, since relatively few comments have been written in Italian. From the remaining German and French data we randomly selected a percentage of respondents as validation or as test respondents.
As a result we received one training set, one validation set and four test sets. The sizes of the sets are listed in Table TABREF8. We did not consider test sets that are cross-lingual and cross-target at the same time, as they would have been too small to yield significant results.
We designated Italian as a test-only language, since relatively few comments have been written in Italian.
Answer: Because relatively few comments have been written in Italian.
Question: What ingredients are no longer used in ketchup?
Evidence: Ketchup or catsup is a table condiment with a sweet and sour flavor. The unmodified term ("ketchup") now typically refers to tomato ketchup, although early recipes used egg whites, mushrooms, oysters, grapes, mussels, or walnuts, among other ingredients.

Tomato ketchup is made from tomatoes, sugar, and vinegar, with seasonings and spices. The spices and flavors vary, but commonly include onions, allspice, coriander, cloves, cumin, garlic, and mustard, and sometimes include celery, cinnamon, or ginger.[citation needed] The market leader in the United States (60% market share) and the United Kingdom (82%) is Heinz Tomato Ketchup. Tomato ketchup is most often used as a condiment to dishes that are usually served hot and are fried or greasy: french fries and other potato dishes, hamburgers, hot dogs, chicken tenders, hot sandwiches, meat pies, cooked eggs, and grilled or fried meat. Ketchup is sometimes used as the basis for, or as one ingredient in, other sauces and dressings, and the flavor may be replicated as an additive flavoring for snacks, such as potato chips.
Answer: Ketchup used to contain many different ingredients that created the sweet and sour flavor traditionally associated with tomato ketchup, including egg whites, mushrooms, oysters, grapes, mussels, or walnuts.
Question: Kate's parents have three kids. The first kid's name is Peter, the second kid's name is Parker. What is the third kid's name?
Answer: Kate's parents have three kids. So apparently Kate is one of them. We know the other two kids' name is Peter and Parker, so the third kid is Kate herself. And name is Kate!
Question: How much better is performance of proposed method than state-of-the-art methods in experiments?
Evidence: Experimental results of entity classification on the test sets of all the datasets is shown in Table TABREF25. The results is clearly demonstrate that our proposed method significantly outperforms state-of-art results on accuracy for three datasets. For more in-depth performance analysis, we note: (1) Among all baselines, Path-based methods and Attribute-incorporated methods outperform three typical methods. This indicates that incorporating extra information can improve the knowledge graph embedding performance; (2) Four variants of KANE always outperform baseline methods. The main reasons why KANE works well are two fold: 1) KANE can capture high-order structural information of KGs in an efficient, explicit manner and passe these information to their neighboring; 2) KANE leverages rich information encoded in attribute triples. These rich semantic information can further improve the performance of knowledge graph; (3) The variant of KANE that use LSTM Encoder and Concatenation aggregator outperform other variants. The main reasons is that LSTM encoder can distinguish the word order and concatenation aggregator combine all embedding of multi-head attention in a higher leaver feature space, which can obtain sufficient expressive power.
FLOAT SELECTED: Table 2: Entity classification results in accuracy. We run all models 10 times and report mean ± standard deviation. KANE significantly outperforms baselines on FB24K, DBP24K and Game30K.
Experimental results of entity classification on the test sets of all the datasets is shown in Table TABREF25. The results is clearly demonstrate that our proposed method significantly outperforms state-of-art results on accuracy for three datasets.
FLOAT SELECTED: Table 2: Entity classification results in accuracy. We run all models 10 times and report mean ± standard deviation. KANE significantly outperforms baselines on FB24K, DBP24K and Game30K.
Answer: Accuracy of best proposed method KANE (LSTM+Concatenation) are 0.8011, 0.8592, 0.8605 compared to best state-of-the art method R-GCN + LR 0.7721, 0.8193, 0.8229 on three datasets respectively.
Question: What is Matong
Evidence: Matong /məˈtɒŋ/ is a town in the central east part of the Riverina region of New South Wales, Australia. The town is 37 kilometres (23 mi) east of Narrandera and 26 kilometres (16 mi) west of Coolamon. At the 2016 census, Matong had a population of 164 people.

Sport
The most popular sport in Matong is Australian rules football, as it lies in the narrow 'canola belt', a geographical triangle stretching from the Grong Grong and Marrar at either end of the Canola Way, to Ungarie, in which Australian football retains a strong following, despite New South Wales being a largely rugby league supporting state.
Answer: Matong is a town in the central east part of the Riverina region of New South Wales, Australia. The town is 37 kilometres (23 mi) east of Narrandera and 26 kilometres (16 mi) west of Coolamon. At the 2016 census, Matong had a population of 164 people.

Sport
The most popular sport in Matong is Australian rules football, as it lies in the narrow 'canola belt', a geographical triangle stretching from the Grong Grong and Marrar at either end of the Canola Way, to Ungarie, in which Australian football retains a strong following, despite New South Wales being a largely rugby league supporting state.
Question: What can be done by their system?
Evidence: We described a method for monolingual and crosslingual clustering of an incoming stream of documents. The method works by maintaining centroids for the monolingual and crosslingual clusters, where a monolingual cluster groups a set of documents and a crosslingual cluster groups a set of monolingual clusters. We presented an online crosslingual clustering method which auto-corrects past decisions in an efficient way. We showed that our method gives state-of-the-art results on a multilingual news article dataset for English, Spanish and German. Finally, we discussed how to leverage different SVM training procedures for ranking and classification to improve monolingual and crosslingual clustering decisions. Our system is integrated in a larger media monitoring project BIBREF10 , BIBREF11 and solving the use-cases of monitors and journalists, having been validated with qualitative user testing.
Our system is integrated in a larger media monitoring project (Liepins et al., 2017; Germann et al., 2018) and solving the usecases of monitors and journalists, having been validated with qualitative user testing.
Answer: Solving the usecases of monitors and journalists.
Question: Where does the molecular formula of D-allulose?
Answer: The molecular formula for D-allulose, also referred to as psicose or allulose is C6H12O6.
Question: Would you add contents to review, discuss, and evaluate against another group of related methods for designing hardware accelerators?
Evidence: Table 14: The comparison between the accelerator designs suggested by PRIME and EdgeTPU (Yazdanbakhsh et al., 2021b; Gupta & Akin, 2020) for single model specialization. On average (last row), with single-model specialization our method reduces the latency by 2.69× while minimizes the chip area usage by 1.50×.
Answer: We have now added comparisons between the accelerators found by PRIME and the EdgeTPU accelerator, which is a prior accelerator designed by human experts, aimed towards accelerating image-classification tasks in Appendix A.7. As shown in Table 14, across all the nine applications we study in this paper, we find that PRIME finds accelerators that are 2.69 \times (up to 11.84\times in t-RNN Enc) better than the EdgeTPU accelerator in terms of latency. PRIME attains this improvement on average, reducing the chip area usage by 1.50\times (up to 2.28\times in MobileNetV3). Even when evaluated only on the MobileNet image-classification domains, we attain an average improvement of 1.85 in terms of latency. This indicates that PRIME is effective when compared to the human-engineered EdgeTPU accelerator.
Question: Given the reference text below, what is a GATR?
Evidence: The Guided Advanced Tactical Rocket (GATR) is a weapons system under development by Orbital ATK and Elbit Systems. It is intended to provide a low-cost guided missile compatible with existing unguided 70mm rocket launch platforms such as the Hydra 70. The penetrating version of the GATR is equipped with a steel-cased M282 warhead and a programmable fuze. It can be set from the cockpit to detonate on impact or after a delay and is capable of penetrating a triple-brick wall and light-vehicle armor.
Answer: A Guided Advanced Tactical Rocket (GATR) is a low-cost guided missile equipped with a steel-cased M282 warhead and a programmable fuze.
Question: Should I immigrate to Canada?
Answer: Canada is the third largest country in the world by landmass. It set a record in 2022 for most immigrants into the country, in large part to public health system, quality of living, and low crime rates. That said, Canada is experiencing very high real estate prices in major cities like Toronto and Vancouver due to lack of supply, making it very difficult for families to get ahead.
Question: How do I get better at shooting a basketball?
Answer: Here are a few things you can try to get better at shooting a basketball:

- Practice the fundamentals by repeatedly bringing the ball to your set point without shooting.

- Shoot from < 5 feet away from the basket until you can reliably make the shot.

- Practice your free throws.

- Try shooting off the dribble.

- Get comfortable shooting threes.
Question: How many professional basketball teams are based in California?
Answer: California has 4 basketball teams: the Golden State Warriors, the Los Angeles Clippers, the Los Angeles Lakers, and the Sacramento Kings.
Question: What is the best food place in Bangalore?
Answer: You can always try out Kritunga Briyani if you love eating spicy food, else you are looking for a bear place try Iron Hill or Toit.
Question: How much do they outperform previous state-of-the-art?
Evidence: Results on SemEval-2014 are presented in Table TABREF35 and Table TABREF36 . We find that BERT-single has achieved better results on these two subtasks, and BERT-pair has achieved further improvements over BERT-single. The BERT-pair-NLI-B model achieves the best performance for aspect category detection. For aspect category polarity, BERT-pair-QA-B performs best on all 4-way, 3-way, and binary settings.
FLOAT SELECTED: Table 4: Test set results for Semeval-2014 task 4 Subtask 3: Aspect Category Detection. We use the results reported in XRCE (Brun et al., 2014) and NRC-Canada (Kiritchenko et al., 2014).
FLOAT SELECTED: Table 5: Test set accuracy (%) for Semeval-2014 task 4 Subtask 4: Aspect Category Polarity. We use the results reported in XRCE (Brun et al., 2014), NRCCanada (Kiritchenko et al., 2014) and ATAE-LSTM (Wang et al., 2016). “-” means not reported.
Results on SemEval-2014 are presented in Table TABREF35 and Table TABREF36 . We find that BERT-single has achieved better results on these two subtasks, and BERT-pair has achieved further improvements over BERT-single. The BERT-pair-NLI-B model achieves the best performance for aspect category detection. For aspect category polarity, BERT-pair-QA-B performs best on all 4-way, 3-way, and binary settings.
FLOAT SELECTED: Table 4: Test set results for Semeval-2014 task 4 Subtask 3: Aspect Category Detection. We use the results reported in XRCE (Brun et al., 2014) and NRC-Canada (Kiritchenko et al., 2014).
FLOAT SELECTED: Table 5: Test set accuracy (%) for Semeval-2014 task 4 Subtask 4: Aspect Category Polarity. We use the results reported in XRCE (Brun et al., 2014), NRCCanada (Kiritchenko et al., 2014) and ATAE-LSTM (Wang et al., 2016). “-” means not reported.
Answer: On subtask 3 best proposed model has F1 score of 92.18 compared to best previous F1 score of 88.58.
On subtask 4 best proposed model has 85.9, 89.9 and 95.6 compared to best previous results of 82.9, 84.0 and 89.9 on 4-way, 3-way and binary aspect polarity.
Question: Which word embeddings are analysed?
Evidence: In BIBREF14 , several word embedding models trained in a large Portuguese corpus are evaluated. Within the Word2Vec model, two training strategies were used. In the first, namely Skip-Gram, the model is given the word and attempts to predict its neighboring words. The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. The latter was chosen for application in the present proposal.
The second, Continuous Bag-of-Words (CBOW), the model is given the sequence of words without the middle one and attempts to predict this omitted word. 
Answer: The answers are shown as follows:
* Continuous Bag-of-Words (CBOW)
Question: Classify these wild mushrooms as poisonous or edible: jack-o-lanterns, chanterelles, morels, death caps, destroying angels, lion's mane, false morels, maitake mushroom, oyster mushrooms
Answer: Chanterelles, morels, lion's mane, maitake, and oyster mushrooms are edible mushrooms. While you should avoid these deadly mushrooms such as jack-o-lanterns, death caps, destroying angels, and false morels.