DataHammer/scimrc huggingface dataset:
Question: Does the gradient-based approach outperform both the best black-box method and other comparative methods?
Evidence: Neural machine translation (NMT) has achieved the state-of-the-art results on a mass of language pairs with varying structural differences, such as English-French BIBREF0, BIBREF1 and Chinese-English BIBREF2. However, so far not much is known about how and why NMT works, which pose great challenges for debugging NMT models and designing optimal architectures.
The understanding of NMT models has been approached primarily from two complementary perspectives. The first thread of work aims to understand the importance of representations by analyzing the linguistic information embedded in representation vectors BIBREF3, BIBREF4 or hidden units BIBREF5, BIBREF6. Another direction focuses on understanding the importance of input words by interpreting the input-output behavior of NMT models. Previous work BIBREF7 treats NMT models as black-boxes and provides explanations that closely resemble the attention scores in NMT models. However, recent studies reveal that attention does not provide meaningful explanations since the relationship between attention scores and model output is unclear BIBREF8.
In this paper, we focus on the second thread and try to open the black-box by exploiting the gradients in NMT generation, which aims to estimate the word importance better. Specifically, we employ the integrated gradients method BIBREF9 to attribute the output to the input words with the integration of first-order derivatives. We justify the gradient-based approach via quantitative comparison with black-box methods on a couple of perturbation operations, several language pairs, and two representative model architectures, demonstrating its superiority on estimating word importance.
We analyze the linguistic behaviors of words with the importance and show its potential to improve NMT models. First, we leverage the word importance to identify input words that are under-translated by NMT models. Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods. Second, we analyze the linguistic roles of identified important words, and find that words of certain syntactic categories have higher importance while the categories vary across language. For example, nouns are more important for Chinese$\Rightarrow $English translation, while prepositions are more important for English-French and -Japanese translation. This finding can inspire better design principles of NMT architectures for different language pairs. For instance, a better architecture for a given language pair should consider its own language characteristics.
Experimental results show that the gradient-based approach outperforms both the best black-box method and other comparative methods.
Answer: Yes.
Question: What is one Earth day?
Answer: One day on Earth is the time for the Earth to completely rotate on its axis.
Question: What are the10 best NCAA Basketball programs all-time?
Answer: UCLA, Kansas, Duke, North Carolina, Connecticut, Michigan State, Kentucky, Louisville, Indiana and Arizona
Question: By how much does using phonetic feedback improve state-of-the-art systems?
Evidence: In addition to the setting without any parallel data, we show results given parallel data. In Table TABREF10 we demonstrate that training the AECNN framework with mimic loss improves intelligibility over both the model trained with only time-domain loss (AECNN-T), as well as the model trained with both time-domain and spectral-domain losses (AECNN-T-SM). We only see a small improvement in the SI-SDR, likely due to the fact that the mimic loss technique is designed to improve the recognizablity of the results. In fact, seeing any improvement in SI-SDR at all is a surprising result.
FLOAT SELECTED: Table 2. Speech enhancement scores for the state-of-the-art system trained with the parallel data available in the CHiME4 corpus. Evaluation is done on channel 5 of the simulation et05 data. Mimic loss is applied to the AECNN model trained with time-domain mapping loss only, as well as time-domain and spectral magnitude mapping losses. The joint training system is done with an identical setup to the mimic system with all three losses.
In addition to the setting without any parallel data, we show results given parallel data. In Table TABREF10 we demonstrate that training the AECNN framework with mimic loss improves intelligibility over both the model trained with only time-domain loss (AECNN-T), as well as the model trained with both time-domain and spectral-domain losses (AECNN-T-SM). We only see a small improvement in the SI-SDR, likely due to the fact that the mimic loss technique is designed to improve the recognizablity of the results. In fact, seeing any improvement in SI-SDR at all is a surprising result.
FLOAT SELECTED: Table 2. Speech enhancement scores for the state-of-the-art system trained with the parallel data available in the CHiME4 corpus. Evaluation is done on channel 5 of the simulation et05 data. Mimic loss is applied to the AECNN model trained with time-domain mapping loss only, as well as time-domain and spectral magnitude mapping losses. The joint training system is done with an identical setup to the mimic system with all three losses.
Answer: Improved AECNN-T by 2.1 and AECNN-T-SM BY 0.9
Question: Who built Sigiriya fortress in Sri Lanka?
Evidence: It is likely that the area around Sigiriya may have been inhabited since prehistoric times. There is clear evidence that the many rock shelters and caves in the vicinity were occupied by Buddhist monks and ascetics from as early as the 3rd century BC. The earliest evidence of human habitation at Sigiriya is the Aligala rock shelter to the east of Sigiriya rock, indicating that the area was occupied nearly five thousand years ago during the Mesolithic Period.

Buddhist monastic settlements were established during the 3rd century BC in the western and northern slopes of the boulder-strewn hills surrounding the Sigiriya rock. Several rock shelters or caves were created during this period. These shelters were made under large boulders, with carved drip ledges around the cave mouths. Rock inscriptions are carved near the drip ledges on many of the shelters, recording the donation of the shelters to the Buddhist monastic order as residences. These were made in the period between the 3rd century BCE and the 1st century AD.

In 477 AD, Kashyapa I, the king’s son by a non-royal consort, seized the throne from King Dhatusena, following a coup assisted by Migara, the King’s nephew and army commander. The rightful heir, Moggallana, fearing for his life, fled to South India. Afraid of an attack by Moggallana, Kashyapa moved the capital and his residence from the traditional capital of Anuradhapura to the more secure Sigiriya. During King Kashyapa’s reign (477 to 495 AD), Sigiriya was developed into a complex city and fortress. Most of the elaborate constructions on the rock summit and around it, including defensive structures, palaces, and gardens, date from this period.
Answer: King Kashyapa developed Sigiriya city and fortress.
Question: What was "the Dry Capital of the World"?
Evidence: Westerville is a city in Franklin and Delaware counties in the U.S. state of Ohio. A northeastern suburb of Columbus, the population was 39,190 at the 2020 census. Westerville is the home of Otterbein University. Westerville was once known as "The Dry Capital of the World" for its strict laws prohibiting sales of alcohol and for being the home of the Anti-Saloon League, one of the driving forces behind Prohibition at the beginning of the 20th century.
Answer: Westerville, OH was once known as "The Dry Capital of the World" due to being the home of the Anti-Saloon League, a driving force behind Prohibition in the early 1900s, and for its strict laws prohibiting sales of alcohol. Ironically, there are now many restaurants and a small brewery in Westerville that serve alcohol.
Question: How to be a good human being?
Answer: Always be thoughtful of others. Put yourselves in the other persons shoes. Do not judge anyone based on their color, appearance, ethnicity, education, job etc. Their actions may not look appropriate , but a lot of times it is because they do not know and they are still learning. Be kind and help such folks. If everyone starts doing this , the world would be a much happier place. The happiest places in the world are not with the best weather and infrastructure, it is the happiness of the people living there.
Question: In the series A Song of Ice and Fire, who is the founder of House Qoherys?
Answer: Quenton Qoherys
Question: When will we go interplanetary?
Answer: Humans already have successful missions to other planets. There are project that are in progress across world that should put humans in Mars in next decade. If you ask me, I am already in Mars and attempting to go intergalaxy.
Question: What are the three limitations?
Evidence: In this part, we discuss three significant limitations of BLEU and ROUGE. These metrics can assign: High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.
Challenges with BLEU and ROUGE ::: High score, opposite meanings
Suppose that we have a reference summary s1. By adding a few negation terms to s1, one can create a summary s2 which is semantically opposite to s1 but yet has a high BLEU/ROUGE score.
Challenges with BLEU and ROUGE ::: Low score, similar meanings
In addition not to be sensitive to negation, BLEU and ROUGE score can give low scores to sentences with equivalent meaning. If s2 is a paraphrase of s1, the meaning will be the same ;however, the overlap between words in s1 and s2 will not necessarily be significant.
Challenges with BLEU and ROUGE ::: High score, unintelligible sentences
A third weakness of BLEU and ROUGE is that in their simplest implementations, they are insensitive to word permutation and can give very high scores to unintelligible sentences. Let s1 be "On a morning, I saw a man running in the street." and s2 be “On morning a, I saw the running a man street”. s2 is not an intelligible sentence. The unigram version of ROUGE and BLEU will give these 2 sentences a score of 1.
In this part, we discuss three significant limitations of BLEU and ROUGE. These metrics can assign: High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.

Challenges with BLEU and ROUGE ::: High score, opposite meanings
Suppose that we have a reference summary s1. By adding a few negation terms to s1, one can create a summary s2 which is semantically opposite to s1 but yet has a high BLEU/ROUGE score.

Challenges with BLEU and ROUGE ::: Low score, similar meanings
In addition not to be sensitive to negation, BLEU and ROUGE score can give low scores to sentences with equivalent meaning. If s2 is a paraphrase of s1, the meaning will be the same ;however, the overlap between words in s1 and s2 will not necessarily be significant.

Challenges with BLEU and ROUGE ::: High score, unintelligible sentences
A third weakness of BLEU and ROUGE is that in their simplest implementations, they are insensitive to word permutation and can give very high scores to unintelligible sentences. Let s1 be "On a morning, I saw a man running in the street." and s2 be “On morning a, I saw the running a man street”. s2 is not an intelligible sentence. The unigram version of ROUGE and BLEU will give these 2 sentences a score of 1.
Answer: The answers are shown as follows:
* High scores to semantically opposite translations/summaries, Low scores to semantically related translations/summaries and High scores to unintelligible translations/summaries.
Question: What are the best types of questions to ask during a job interview?
Answer: The best types of questions to ask during an interview will depend on the position being filled, the required experience and expertise, and the specific skill set sought. It is important to ask background questions to understand the candidate’s current role and responsibilities, as well as their ideal next job. Behavioral questions are also quite useful during an interview. Behavioral questions ask a candidate to describe how they would respond in a specific situation and can be tailored to the skills and experience sought. For example, it is useful to describe a situation the candidate is likely to encounter at the potential new job and ask them to describe how they would handle the situation. This will help you identify whether the candidate has the skills and experience to address some of the day-to-day work at your company. It is a good idea to ask multiple behavioral questions during each interview and to ask all candidates for that role the same questions.
Question: Based on the reference text about McDonalds, tell me what year it was founded, how many daily customers they have, and their best selling product.
Evidence: McDonald's Corporation is an American multinational fast food chain, founded in 1940 as a restaurant operated by Richard and Maurice McDonald, in San Bernardino, California, United States. They rechristened their business as a hamburger stand, and later turned the company into a franchise, with the Golden Arches logo being introduced in 1953 at a location in Phoenix, Arizona. In 1955, Ray Kroc, a businessman, joined the company as a franchise agent and proceeded to purchase the chain from the McDonald brothers. McDonald's had its previous headquarters in Oak Brook, Illinois, but moved its global headquarters to Chicago in June 2018.

McDonald's is the world's largest fast food restaurant chain, serving over 69 million customers daily in over 100 countries in more than 40,000 outlets as of 2021. McDonald's is best known for its hamburgers, cheeseburgers and french fries, although their menu also includes other items like chicken, fish, fruit, and salads. Their best-selling licensed item are their french fries, followed by the Big Mac. The McDonald's Corporation revenues come from the rent, royalties, and fees paid by the franchisees, as well as sales in company-operated restaurants. McDonald's is the world's second-largest private employer with 1.7 million employees (behind Walmart with 2.3 million employees). As of 2022, McDonald's has the sixth-highest global brand valuation.

McDonald's has been subject to criticism over the health effects of its products, its treatment of employees, and other business practices.
Answer: McDonalds was founded in 1940 and serves over 69 million customers a day as of 2021. It's best selling product are their french fries.
Question: Name some famous Schubert's pieces
Answer: In his esteemed but rather short life span, Schubert wrote some of the most beautiful and poetic compositions. Some of the well renowned compositions are "Piano Quintet In A", "Winterreise", "Piano Sonata No.21", and "Symphony No.8 In B minor" which is also referred as 'Unfinished' Symphony because Schubert unfortunately died right before completing it.
Question: What is home automation and how can it be used?
Answer: Home automation is the process of installing various interactive components in a home as a way to centralize the management of these components through an integrated interface such as a phone, table, computer, or dedicated virtual assistant such as Alexa.  The advantage of home automation is that the various arrays of smart devices available on the market today can be programmed on a daily, weekly, or hourly schedule.   These devices can include lights, switches, plugs, appliances, televisions, garage door openers, locks, and security cameras.  Through voice assistants like Alexa you can name your devices and verbally issue commands that the voice assistant will use to action with the targeted smart device.  The applications for this technology are extensive including allowing for remote monitoring for security purposes, ensuring minimal energy consumption in your home, providing remote access to your home while away, and simply automating the use of your residence.
Question: What is the problem that the authors hope to ameliorate?
Evidence: Recent years have seen unprecedented progress for Natural Language Processing (NLP) on almost every NLP subtask. Even though low-resource settings have also been explored, this progress has overwhelmingly been observed in languages with significant data resources that can be leveraged to train deep neural networks. Low-resource languages still lag behind.
Endangered languages pose an additional challenge. The process of documenting an endangered language typically includes the creation of word lists, audio and video recordings, notes, or grammar fragments, with the created resources then stored in large online linguistics archives. This process is often hindered by the Transcription Bottleneck: the linguistic fieldworker and the language community may not have time to transcribe all of the recordings and may only transcribe segments that are linguistically salient for publication or culturally significant for the creation of community resources.
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina. We hope to ameliorate the resource gap and the transcription bottleneck in two ways. First, we are providing a larger data set than has previously been available, and second, we are providing baselines for NLP tasks (speech recognition, speech synthesis, and machine translation). In providing baselines and datasets splits, we hope to further facilitate research on low-resource NLP for this language through our data set. Research on low-resource speech recognition is particularly important in relieving the transcription bottleneck, while tackling the research challenges that speech synthesis and machine translation pose for such languages could lead to such systems being deployed to serve more under-represented communities.
With this work we make publicly available a large corpus in Mapudungun, a language of the indigenous Mapuche people of southern Chile and western Argentina. We hope to ameliorate the resource gap and the transcription bottleneck in two ways.
Answer: The authors hope to ameliorate the resource gap and the transcription bottleneck of Mapudungun.
Question: What representation is used in the paper?
Evidence: Reading Comprehension (RC) has become a central task in natural language processing, with great practical value in various industries. In recent years, many large-scale RC datasets in English BIBREF0, BIBREF1, BIBREF2, BIBREF3, BIBREF4, BIBREF5, BIBREF6 have nourished the development of numerous powerful and diverse RC models BIBREF7, BIBREF8, BIBREF9, BIBREF10, BIBREF11. The state-of-the-art model BIBREF12 on SQuAD, one of the most widely used RC benchmarks, even surpasses human-level performance. Nonetheless, RC on languages other than English has been limited due to the absence of sufficient training data. Although some efforts have been made to create RC datasets for Chinese BIBREF13, BIBREF14 and Korean BIBREF15, it is not feasible to collect RC datasets for every language since annotation efforts to collect a new RC dataset are often far from trivial. Therefore, the setup of transfer learning, especially zero-shot learning, is of extraordinary importance.
Existing methods BIBREF16 of cross-lingual transfer learning on RC datasets often count on machine translation (MT) to translate data from source language into target language, or vice versa. These methods may not require a well-annotated RC dataset for the target language, whereas a high-quality MT model is needed as a trade-off, which might not be available when it comes to low-resource languages.
In this paper, we leverage pre-trained multilingual language representation, for example, BERT learned from multilingual un-annotated sentences (multi-BERT), in cross-lingual zero-shot RC. We fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair to explore the cross-lingual ability of multi-BERT. Surprisingly, we find that the models have the ability to transfer between low lexical similarity language pair, such as English and Chinese. Recent studies BIBREF17, BIBREF12, BIBREF18 show that cross-lingual language models have the ability to enable preliminary zero-shot transfer on simple natural language understanding tasks, but zero-shot transfer of RC has not been studied. To our knowledge, this is the first work systematically exploring the cross-lingual transferring ability of multi-BERT on RC tasks.
In this paper, we leverage pre-trained multilingual language representation, for example, BERT learned from multilingual un-annotated sentences (multi-BERT), in cross-lingual zero-shot RC. We fine-tune multi-BERT on the training set in source language, then test the model in target language, with a number of combinations of source-target language pair to explore the cross-lingual ability of multi-BERT.
Answer: Pre-trained multilingual language representation.
Question: Is the new model evaluated on the tasks that BERT and ELMo are evaluated on?
Evidence: FLOAT SELECTED: Table 1: GLUE results with and without STILTs, fine-tuning on full training data of each target task. Bold marks the best within each section. Strikethrough indicates cases where the intermediate task is the same as the target task—we substitute the baseline result for that cell. A.Ex is the average excluding MNLI and QQP because of the overlap with intermediate tasks. See text for discussion of WNLI results. Test results on STILTs uses the supplementary training regime for each task based on the performance on the development set, corresponding to the numbers shown in Best of Each. The aggregated GLUE scores differ from the public leaderboard because we report performance on QNLIv1.
FLOAT SELECTED: Table 1: GLUE results with and without STILTs, fine-tuning on full training data of each target task. Bold marks the best within each section. Strikethrough indicates cases where the intermediate task is the same as the target task—we substitute the baseline result for that cell. A.Ex is the average excluding MNLI and QQP because of the overlap with intermediate tasks. See text for discussion of WNLI results. Test results on STILTs uses the supplementary training regime for each task based on the performance on the development set, corresponding to the numbers shown in Best of Each. The aggregated GLUE scores differ from the public leaderboard because we report performance on QNLIv1.
Answer: Yes.
Question: Which is a species of fish? Slipmouth or Slipknot
Answer: Slipmouth
Question: What does BAFTA stands for ?
Answer: British Academy of Film and Television Arts
Question: Are you going for a walk today?
Answer: These rainy days haven't been the greatest, but now with that shining sun, it'd be a shame to stay in!
Question: Who prepares the Global Hunger Index?
Answer: Global Hunger Index is jointly prepared by Welhunger Life and Concern Worldwide
Question: How many Spitfire fighter aircraft remain airworthy
Evidence: The Supermarine Spitfire is a British single-seat fighter aircraft used by the Royal Air Force and other Allied countries before, during, and after World War II. Many variants of the Spitfire were built, from the Mk 1 to the Rolls-Royce Griffon-engined Mk 24 using several wing configurations and guns. It was the only British fighter produced continuously throughout the war. The Spitfire remains popular among enthusiasts; around 70 remain airworthy, and many more are static exhibits in aviation museums throughout the world.
Answer: around 70 remain airworthy