DataHammer/scimrc huggingface dataset:
Question: What system do they train?
Evidence: Unsupervised learning of cross-lingual word embedding offers elegant matching of words across languages, but has fundamental limitations in translating sentences. In this paper, we propose simple yet effective methods to improve word-by-word translation of cross-lingual embeddings, using only monolingual corpora but without any back-translation. We integrate a language model for context-aware search, and use a novel denoising autoencoder to handle reordering. Our system surpasses state-of-the-art unsupervised neural translation systems without costly iterative training. We also analyze the effect of vocabulary size and denoising type on the translation performance, which provides better understanding of learning the cross-lingual word embedding and its usage in translation.
Building a machine translation (MT) system requires lots of bilingual data. Neural MT models BIBREF0 , which become the current standard, are even more difficult to train without huge bilingual supervision BIBREF1 . However, bilingual resources are still limited to some of the selected language pairs—mostly from or to English.
A workaround for zero-resource language pairs is translating via an intermediate (pivot) language. To do so, we need to collect parallel data and train MT models for source-to-pivot and pivot-to-target individually; it takes a double effort and the decoding is twice as slow.
Unsupervised learning is another alternative, where we can train an MT system with only monolingual corpora. Decipherment methods BIBREF2 , BIBREF3 are the first work in this direction, but they often suffer from a huge latent hypothesis space BIBREF4 .
Recent work by unmt-artetxe and unmt-facebook train sequence-to-sequence MT models of both translation directions together in an unsupervised way. They do back-translation BIBREF5 back and forth for every iteration or batch, which needs an immensely long time and careful tuning of hyperparameters for massive monolingual data.
Here we suggest rather simple methods to build an unsupervised MT system quickly, based on word translation using cross-lingual word embeddings. The contributions of this paper are:
The proposed models can be efficiently trained with off-the-shelf softwares with little or no changes in the implementation, using only monolingual data. The provided analyses help for better learning of cross-lingual word embeddings for translation purpose. Altogether, our unsupervised MT system outperforms the sequence-to-sequence neural models even without training signals from the opposite translation direction, i.e. via back-translation.
In this paper, we propose simple yet effective methods to improve word-by-word translation of crosslingual embeddings, using only monolingual corpora but without any back-translation.****Altogether, our unsupervised MT system outperforms the sequence-to-sequence neural models even without training signals from the opposite translation direction, i.e. via backtranslation.
Answer: A machine translation (MT) system with only monolingual corpora.
Question: When is tax day?
Answer: Tax day usually refers to the day in the United States when tax forms must be submitted and paid for individual and businesses. It is typically April 15 or later.
Question: What is the tiny piece at the end of a shoelace called?
Answer: An Aglet.
Question: What is the main prerequisite for an AI to surpass human-level intelligence?
Evidence: If a superhuman intelligence were to be invented—either through the amplification of human intelligence or through artificial intelligence—it would vastly improve over human problem-solving and inventive skills. Such an AI is referred to as Seed AI because if an AI were created with engineering capabilities that matched or surpassed those of its human creators, it would have the potential to autonomously improve its own software and hardware to design an even more capable machine, which could repeat the process in turn. This recursive self-improvement could accelerate, potentially allowing enormous qualitative change before any upper limits imposed by the laws of physics or theoretical computation set in. It is speculated that over many iterations, such an AI would far surpass human cognitive abilities.
Answer: An AI system needs to have the capability of recursive self-improvement. Once available such a capability would allow the AI system to accelerate its development at a rapid rate.
Question: How to live a long and healthy life?
Answer: Well, longevity of life is never guaranteed no matter how healthy you live. However, you can lead a healthy life to improve the quality of your life. You should eat a balanced diet and exercise at a minimum of 3 times per week. Research has also shown that having a social circle improves mental health. Investing in people around you and spending time with loved ones can help lower stress and improve satisfaction in life generally.
Question: Which one among the following three words is different to the others: Novelty, Playful, Irresistible? Why?
Answer: The word 'Novelty' is different to the other two words because it is an noun, while the others are adjectives.
Question: Name some of the objects in the solar system
Answer: In addition to the sun and the 8 planets, the solar system includes many different types of objects. Many of the planets are orbited by moons. There are millions of asteroids in the asteroid belt between Mars and Jupiter. Outside the orbit of Neptune, the Kuiper Belt includes comets, dwarf planets, and other icy objects.
Question: What are the strong baselines you have?
Evidence: We optimized our single-task baseline to get a strong baseline in order to exclude better results in multi-task learning in comparison to single-task learning only because of these two following points: network parameters suit the multi-task learning approach better and a better randomness while training in the multi-task learning. To exclude the first point, we tested different hyperparameters for the single-task baseline. We tested all the combinations of the following hyperparameter values: 256, 512, or 1024 as the sizes for the hidden states of the LSTMs, 256, 512, or 1024 as word embedding sizes, and a dropout of 30 %, 40 %, or 50 %. We used subword units generated by byte-pair encoding (BPE) BIBREF16 as inputs for our model. To avoid bad subword generation for the synthetic datasets, in addition to the training dataset, we considered the validation and test dataset for the generating of the BPE merge operations list. We trained the configurations for 14 epochs and trained every configuration three times. We chose the training with the best quality with regard to the validation F1-score to exclude disadvantages of a bad randomness. We got the best quality with regard to the F1-score with 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and a dropout of 30 %. For the batch size, we used 64.
We optimized our single-task baseline to get a strong baseline in order to exclude better results in multi-task learning in comparison to single-task learning only because of these two following points: network parameters suit the multi-task learning approach better and a better randomness while training in the multi-task learning. To exclude the first point, we tested different hyperparameters for the single-task baseline. We tested all the combinations of the following hyperparameter values: 256, 512, or 1024 as the sizes for the hidden states of the LSTMs, 256, 512, or 1024 as word embedding sizes, and a dropout of 30 %, 40 %, or 50 %. We used subword units generated by byte-pair encoding (BPE) BIBREF16 as inputs for our model. To avoid bad subword generation for the synthetic datasets, in addition to the training dataset, we considered the validation and test dataset for the generating of the BPE merge operations list. We trained the configurations for 14 epochs and trained every configuration three times. We chose the training with the best quality with regard to the validation F1-score to exclude disadvantages of a bad randomness. We got the best quality with regard to the F1-score with 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and a dropout of 30 %. For the batch size, we used 64.
Answer: optimize single task with no synthetic data
Question: How big is LibriSpeech dataset?
Evidence: We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset. The paired audio/transcript dataset was used to train T-T models and an LSTM-based baseline. The full 810M word tokens text dataset was used for standalone language model (LM) training. We extracted 128-channel logmel energy values from a 32 ms window, stacked every 4 frames, and sub-sampled every 3 frames, to produce a 512-dimensional acoustic feature vector with a stride of 30 ms. Feature augmentation BIBREF22 was applied during model training to prevent overfitting and to improve generalization, with only frequency masking ($\mathrm {F}=50$, $\mathrm {mF}=2$) and time masking ($\mathrm {T}=30$, $\mathrm {mT}=10$).
We evaluated the proposed model using the publicly available LibriSpeech ASR corpus BIBREF23. The LibriSpeech dataset consists of 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset.
Answer: The answers are shown as follows:
* 970 hours of audio data with corresponding text transcripts (around 10M word tokens) and an additional 800M word token text only dataset
Question: Tell me which one does not fit with the group:  Football, Volleyball, Cricket, Chess, Hockey
Answer: All except Chess are outdoor games.
Question: What are the two strategies when using oos-binary?
Evidence: We use three baseline approaches for the task of predicting whether a query is out-of-scope: (1) oos-train, where we train an additional (i.e. 151st) intent on out-of-scope training data; (2) oos-threshold, where we use a threshold on the classifier's probability estimate; and (3) oos-binary, a two-stage process where we first classify a query as in- or out-of-scope, then classify it into one of the 150 intents if classified as in-scope.
To reduce the severity of the class imbalance between in-scope versus out-of-scope query samples (i.e., 15,000 versus 250 queries for OOS+), we investigate two strategies when using oos-binary: one where we undersample the in-scope data and train using 1,000 in-scope queries sampled evenly across all intents (versus 250 out-of-scope), and another where we augment the 250 OOS+ out-of-scope training queries with 14,750 sentences sampled from Wikipedia.
From a development point of view, the oos-train and oos-binary methods both require careful curation of an out-of-scope training set, and this set can be tailored to individual systems. The oos-threshold method is a more general decision rule that can be applied to any model that produces a probability. In our evaluation, the out-of-scope threshold was chosen to be the value which yielded the highest validation score across all intents, treating out-of-scope as its own intent.
To reduce the severity of the class imbalance between in-scope versus out-of-scope query samples (i.e., 15,000 versus 250 queries for OOS+), we investigate two strategies when using oos-binary: one where we undersample the in-scope data and train using 1,000 in-scope queries sampled evenly across all intents (versus 250 out-of-scope), and another where we augment the 250 OOS+ out-ofscope training queries with 14,750 sentences sampled from Wikipedia.
Answer: One: the authors undersample the in-scope data and train using 1,000 in-scope queries sampled evenly across all intents (versus 250 out-of-scope); Two: the authors augment the 250 OOS+ out of scope training queries with 14,750 sentences sampled from Wikipedia.
Question: What is the east Asian process of training small trees called?
Answer: Bonsai
Question: Why we have different seasons on earth?
Answer: It is because of earth's tilt on its axis. As of now, earth is approximately 23.5 degrees tilted on its axis in the orbit around sun. The side of the earth is closer to sun gets more heat from sun. That is why when it is summer in north hemisphere, it is winter in south hemisphere and vice-versa.
Question: What are some different snacks I could bring to a kids' soccer game?
Answer: You could bring orange slices, juice boxes, trail mix, peanut butter and apples, granola bars or cheese sticks
Question: What do the authors focus on in this paper?
Evidence: Our datasets can be used for various hate speech tasks. In this paper, we focus on generative hate speech intervention.
The goal of this task is to generate a response to hate speech that can mitigate its use during a conversation. The objective can be formulated as the following equation:
where $c$ is the conversation, $r$ is the corresponding intervention response, and $D$ is the dataset. This task is closely related to the response generation and dialog generation, though several differences exist including dialog length, language cadence, and word imbalances. As a baseline, we chose the most common methods of these two tasks, such as Seq2Seq and VAE, to determine the initial feasibility of automatically generate intervention responses. More recent Reinforcement Learning method for dialog generation BIBREF21 can also be applied to this task with slight modification. Future work will explore more complex, and unique models.
Similar to BIBREF21, a generative model is considered as an agent. However, different from dialog generation, generative intervention does not have multiple turns of utterance, so the action of the agent is to select a token in the response. The state of the agent is given by the input posts and the previously generated tokens. Another result due to this difference is that the rewards with regard to ease of answering or information flow do not apply to this case, but the reward for semantic coherence does. Therefore, the reward of the agent is:
where $rw(c,r)$ is the reward with regard to the conversation $c$ and its reference response $r$ in the dataset. $p(r|c)$ denotes the probability of generating response $r$ given the conversation $c$, and $p_{back}(c|r)$ denotes the backward probability of generating the conversation based on the response, which is parameterized by another generation network. The reward is a weighted combination of these two parts, which are observed after the agent finishing generating the response. We refer the readers to BIBREF21 for details.
Our datasets can be used for various hate speech tasks. In this paper, we focus on generative hate speech intervention.
Answer: Generative hate speech intervention.
Question: What causes ocean tides?
Answer: Tides are very long waves moving across the ocean and are caused by the gravitational forces exerted on the earth by the moon. When the highest point in the long-period wave reaches a shoreline the area experiences high tide. When the lowest point of the wave, the trough, reaches shore the area experiences low tide. The earth is mostly water and the water "points" at the moon, the parts of the Earth experiencing high tide is the water drawing towards the moon. The "pointing" water that faces the moon is formed because the gravitational pull of the moon is strongest on whichever side of the Earth faces it. Gravity pulls the ocean towards the moon and high tide occurs.

The bulge on the far side of the Earth is caused by inertia. The water moving away from the moon resists the gravitational forces that attempt to pull it in the opposite direction. Because the gravitational pull of the moon is weaker on the far side of the Earth, inertia wins, the ocean bulges out and high tide occurs. As the Earth spins, different areas of the planet face the moon, and this rotation causes the tides to cycle around the planet.
Question: Given this paragraph about Pablo Escobar, how much money did he have when he died?
Evidence: Pablo Emilio Escobar Gaviria (/ˈɛskəbɑːr/; Spanish: [ˈpaβlo es.koˈβ̞aɾ]; 1 December 1949 – 2 December 1993) was a Colombian drug lord and narcoterrorist who was the founder and sole leader of the Medellín Cartel. Dubbed "the king of cocaine", Escobar was the wealthiest criminal in history, having amassed an estimated net worth of US$30 billion by the time of his death—equivalent to $70 billion as of 2022—while his drug cartel monopolized the cocaine trade into the United States in the 1980s and early 1990s.
Answer: Pablo Escobar had an estimated net worth of $30B when he died in 1993.
Question: What does the phrase "the Carolinas" refer to?
Answer: When someone says "the Carolinas," they are referring to both the state of North Carolina and the state of South Carolina within the United States.
Question: What is the baseline of this paper?
Evidence: Writing errors can occur in many different forms – from relatively simple punctuation and determiner errors, to mistakes including word tense and form, incorrect collocations and erroneous idioms. Automatically identifying all of these errors is a challenging task, especially as the amount of available annotated data is very limited. Rei2016 showed that while some error detection algorithms perform better than others, it is additional training data that has the biggest impact on improving performance.
Being able to generate realistic artificial data would allow for any grammatically correct text to be transformed into annotated examples containing writing errors, producing large amounts of additional training examples. Supervised error generation systems would also provide an efficient method for anonymising the source corpus – error statistics from a private corpus can be aggregated and applied to a different target text, obscuring sensitive information in the original examination scripts. However, the task of creating incorrect data is somewhat more difficult than might initially appear – naive methods for error generation can create data that does not resemble natural errors, thereby making downstream systems learn misleading or uninformative patterns.
Previous work on artificial error generation (AEG) has focused on specific error types, such as prepositions and determiners BIBREF0 , BIBREF1 , or noun number errors BIBREF2 . Felice2014a investigated the use of linguistic information when generating artificial data for error correction, but also restricting the approach to only five error types. There has been very limited research on generating artificial data for all types, which is important for general-purpose error detection systems. For example, the error types investigated by Felice2014a cover only 35.74% of all errors present in the CoNLL 2014 training dataset, providing no additional information for the majority of errors.
In this paper, we investigate two supervised approaches for generating all types of artificial errors. We propose a framework for generating errors based on statistical machine translation (SMT), training a model to translate from correct into incorrect sentences. In addition, we describe a method for learning error patterns from an annotated corpus and transplanting them into error-free text. We evaluate the effect of introducing artificial data on two error detection benchmarks. Our results show that each method provides significant improvements over using only the available training set, and a combination of both gives an absolute improvement of 4.3% in INLINEFORM0 , without requiring any additional annotated data.
 
We propose a framework for generating errors based on statistical machine translation (SMT), training a model to translate from correct into incorrect sentences.
Answer: Statistical machine translation (SMT).
Question: What is the attraction in the United States for the Jeep Wrangler?
Answer: The Jeep Wrangler represents the current version of the original Jeep Willys platform which was first released in 1940 for the U.S. Army.  The Jeep Wrangler platform first became available in 1987 and has become one of the most recognizable vehicles manufactured today.  The love Wrangler owners have for their vehicles stems from the utilitarian nature of the platform, the ability to remove the top and doors in warmer weather, the wide array of aftermarket modifications available, as well as its extensive off road capabilities directly off the showroom floor. Jeep enthusiasts, also known as “Jeepers,” often wave at each other when passing in opposite directions.  This tradition stemmed from the use of the Willy’s model in the US military where out of reverence and courtesy passing soldiers driving their Jeeps would wave or salute each other depending on rank.  This tradition continues today amongst Wrangler owners and has been affectionately dubbed the “Jeep Wave.”
Question: how to swim in ocean
Answer: Swimming in the ocean can be an exhilarating experience, but it's important to take proper safety precautions to ensure that you stay safe. Here are some steps to follow to help you swim in the ocean:

Choose a Safe Location: Before entering the water, make sure you choose a safe location to swim. Look for a spot that's away from strong currents, riptides, and waves. Avoid swimming alone, and make sure there are other people around in case you need help.

Check the Conditions: Before entering the water, check the weather and ocean conditions. Avoid swimming if there are strong winds, high waves, or thunderstorms. If there are lifeguards on duty, ask them about the current conditions and any potential hazards.

Wear Proper Gear: Make sure you wear proper swimwear, such as a wetsuit or rash guard, to protect your skin from the sun and ocean elements. Consider wearing fins or a snorkel to help you swim more efficiently.

Practice Good Swimming Technique: When swimming in the ocean, use good swimming technique to help conserve your energy. Avoid swimming against the current, and swim parallel to the shore if you need to move sideways. Use a slow, steady stroke to conserve energy.

Be Aware of Marine Life: When swimming in the ocean, be aware of any marine life that may be in the water. Avoid swimming near schools of fish, and stay away from jellyfish and other potentially dangerous creatures. If you do encounter a shark or other predator, stay calm and swim away slowly.

Follow Ocean Safety Rules: Finally, follow any ocean safety rules that are in place. For example, some beaches may have specific areas designated for swimming, while others may require you to wear a life jacket or avoid swimming during certain times of day.

By following these steps, you can enjoy a safe and enjoyable swimming experience in the ocean. Remember to stay aware of your surroundings, practice good swimming technique, and take proper safety precautions to ensure that you stay safe.
Question: For whom is Daenerys Targaryen's dragon, Viserion, named for?
Answer: She named Viserion after her late brother, Viserys
Question: What is the difference between a guitar and a bass
Answer: A guitar is a musical instrument that generally houses six strings, while a bass usually has four much thicker strings. A bass guitar outputs lower frequency notes, while a regular guitar can play a wider variety of tones.
Question: Why do people enjoy the beach?
Answer: Many people enjoy taking vacations or day trip to the beach. Beach destinations typically offer warm, sunny weather and lovely scenery. The beach offers a little something for everyone, whether you're looking to relax or be active. Those looking to relax may enjoy reading or laying out in the sun. Those looking for activity may enjoy swimming, surfing, snorkeling, or playing beach volleyball.