DataHammer/scimrc huggingface dataset:
Question: What advantages does their proposed method of quantifying interpretability have over the human-in-the-loop evaluation they compare to?
Evidence: In the word embedding literature, the problem of interpretability has been approached via several different routes. For learning sparse, interpretable word representations from co-occurrence variant matrices, BIBREF21 suggested algorithms based on non-negative matrix factorization (NMF) and the resulting representations are called non-negative sparse embeddings (NNSE). To address memory and scale issues of the algorithms in BIBREF21 , BIBREF22 proposed an online method of learning interpretable word embeddings. In both studies, interpretability was evaluated using a word intrusion test introduced in BIBREF20 . The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension. As an alternative method to incorporate human judgement, BIBREF23 proposed joint non-negative sparse embedding (JNNSE), where the aim is to combine text-based similarity information among words with brain activity based similarity information to improve interpretability. Yet, this approach still requires labor-intensive collection of neuroimaging data from multiple subjects.
In addition to investigating the semantic distribution in the embedding space, a word category dataset can be also used to quantify the interpretability of the word embeddings. In several studies, BIBREF21 , BIBREF22 , BIBREF20 , interpretability is evaluated using the word intrusion test. In the word intrusion test, for each embedding dimension, a word set is generated including the top 5 words in the top ranks and a noisy word (intruder) in the bottom ranks of that dimension. The intruder is selected such that it is in the top ranks of a separate dimension. Then, human editors are asked to determine the intruder word within the generated set. The editors' performances are used to quantify the interpretability of the embedding. Although evaluating interpretability based on human judgements is an effective approach, word intrusion is an expensive method since it requires human effort for each evaluation. Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions.
The word intrusion test is expensive to apply since it requires manual evaluations by human observers separately for each embedding dimension.
Furthermore, the word intrusion test does not quantify the interpretability levels of the embedding dimensions, instead it yields a binary decision as to whether a dimension is interpretable or not. However, using continuous values is more adequate than making binary evaluations since interpretability levels may vary gradually across dimensions.
Answer: it is less expensive and quantifies interpretability using continuous values rather than binary evaluations
Question: What are the best books of the 19th century?
Answer: 1. Pride and Prejudice by Jane Austen
2. Jane Eyre by Charlotte Brontë
3. The Picture of Dorian Gray by Oscar Wilde
4. Wuthering Heights by Emily Brontë
5. Crime and Punishment by Fyodor Dostoevsky
Question: What are the most powerful server CPUs?
Answer: AMD EPYC and Intel Xeon are among the most powerful server CPUs these days.
Question: What is backgammon?
Answer: Backgammon is a two-player board game that is thousands of years old. Each player has fifteen pieces. The objective is to be the first player to move your pieces off of the board. The players take turns rolling dice that determine how many places they can move their pieces. The game is a combination of skill and luck.
Question: What are the different ways to bake sourdough?
Answer: You could bake a sourdough bread in a commercial oven. 
A pizza oven
A dutch oven
In the sun
In a hot car
In a pot of boiling water
On the surface of the sun
Question: How do the author's corpus and annotations differ from others' method?
Evidence: Propaganda has been tackled mostly at the article level. BIBREF3 created a corpus of news articles labelled as propaganda, trusted, hoax, or satire. BIBREF4 experimented with a binarized version of that corpus: propaganda vs. the other three categories. BIBREF5 annotated a large binary corpus of propagandist vs. non-propagandist articles and proposed a feature-based system for discriminating between them. In all these cases, the labels were obtained using distant supervision, assuming that all articles from a given news outlet share the label of that outlet, which inevitably introduces noise BIBREF6.
A related field is that of computational argumentation which, among others, deals with some logical fallacies related to propaganda. BIBREF7 presented a corpus of Web forum discussions with instances of ad hominem fallacy. BIBREF8, BIBREF9 introduced Argotario, a game to educate people to recognize and create fallacies, a by-product of which is a corpus with $1.3k$ arguments annotated with five fallacies such as ad hominem, red herring and irrelevant authority, which directly relate to propaganda.
Unlike BIBREF8, BIBREF9, BIBREF7, our corpus uses 18 techniques annotated on the same set of news articles. Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
The most relevant related work is our own, which is published in parallel to this paper at EMNLP-IJCNLP 2019 BIBREF10 and describes a corpus that is a subset of the one used for this shared task.
Unlike (Habernal et al., 2017, 2018a,b), our corpus uses 18 techniques annotated on the same set of news articles.  Moreover, our annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
Answer: Unlike (Habernal et al., 2017, 2018a,b), their corpus uses 18 techniques annotated on the same set of news articles.  Moreover, their annotations aim at identifying the minimal fragments related to a technique instead of flagging entire arguments.
Question: How is chocolate made?
Answer: Chocolate is made first by hand sorting cacao beans, next the beans are roasted in an oven, next you crack and winnow the cacoa beans to separate the nibs from the husks, next cacao butter is extracted from the beans, next the left over part of the bean is crushed and grinded into a silky smooth texture, next the chocolate is hand poured and left to cool into chocolate bars.
Question: When is the world diabetes day celebrated?
Answer: World diabetes day is celebrated on November 14
Question: Which of the following animals is a marsupial: kangaroo, zebra, or elephant?
Answer: Of the animals listed, kangaroo is the only marsupial.
Question: What do you need for car camping?
Answer: I always pack a tent, sleeping bag, cot, and air mattress. The sleeping bag should be rated for the temperatures you expect, and the tent quality will matter more in rainy conditions. I also pack a camping stove and propane canister, along with a pot, pan, and mess kit. You'll need matches as well if your stove doesn't include a lighting mechanism. The campground will have a water spout, but I've found it convenient to bring a five gallon water container that we keep on the camp site. For a weekend trip, a few changes of clothes, a flashlight or headlamp, and a couple extra batteries will definitely come in handy. We bring folding chairs and wood if the camp site has a fire pit. A cooler with plenty of ice can keep ingredients cold for a weekend trip, and between hiking, cooking, setup and teardown, there's enough to do for a full weekend.
Question: What kind of GPU do they use in pretraining?
Evidence: We use a pre-trained Xnlg with a 10-layer encoder and a 6-layer decoder. For every Transformer layer, we use 1024 hidden units, 8 attention heads, and GELU activations BIBREF26. In the first pre-training stage, we directly use the 15-language pre-trained XLM BIBREF5 to initialize the parameters of our encoder and decoder. In the second stage, we use Wikipedia as the monolingual data for the DAE objective, and MultiUN BIBREF27 as the parallel data for the XAE objective. The DAE loss is trained with a weight of $0.5$. We train a two-language (English/Chinese) and a three-language (English/French/Chinese) Xnlg for two downstream NLG tasks, respectively. Following BIBREF5, we use the tokenizer provided by BIBREF28 for Chinese, and Moses for other languages, respectively. Then the words in all languages are split with a shared subword vocabulary learned by BPE BIBREF29. We use Adam optimizer with a linear warm-up over the first 4,000 steps and linear decay for later steps, and the learning rate is set to $10^{-4}$. The pre-training batch size is 64, and the sequence length is set to 256. It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
For fine-tuning on downstream NLG tasks, we use Adam optimizer with a learning rate of $5\times 10^{-6}$. We set the batch size as 16 and 32 for question generation and abstractive summarization, respectively. When the target language is the same as the language of training data, we fine-tune all parameters. When the target language is different from the language of training data, we fine-tune the Transformer layers of the encoder. We truncate the input sentences to the first 256 tokens. During decoding, we use beam search with beam size of 3, and limit the length of the target sequence to 80 tokens.
It takes about 30 hours to run 23,000 steps for the pre-training procedure by using 4 Nvidia Telsa V100-16GB GPUs.
Answer: 4 Nvidia Telsa V100-16GB GPUs.
Question: What's the relationship of Chinese and Japanese
Answer: Japanese originated from Chinese. They share some of the same Chinese characters.
Question: What is the best way to approach a new cat?
Answer: If a cat is not immediately friendly, one of the best ways to approach a cat is by offering it treats.  Even timid cats can be tempted if you figure out what foods they really like.  Start with a smelly food like tuna fish or tinned chicken, or raw meat cat treats if you have them.  Don't expect the cat to approach you immediately.  Put the treat on the ground and sit nearby.  Be careful not to stare at the cat since that is an aggressive gesture.  If the cat takes the treat, try throwing or laying out another treat, this time a bit closer to you.  Repeat until the cat is nearby.  It will learn to associate you with happiness (food).
Question: Who is Romulo Gallegos?
Evidence: Rómulo Ángel del Monte Carmelo Gallegos Freire (2 August 1884 – 5 April 1969) was a Venezuelan novelist and politician. For a period of nine months during 1948, he governed as the first freely elected president in Venezuela's history. He was removed from power by military officers in the 1948 Venezuelan coup.
Answer: Rómulo Ángel del Monte Carmelo Gallegos Freire (2 August 1884 – 5 April 1969) was a Venezuelan novelist and politician. For a period of nine months during 1948, he governed as the first freely elected president in Venezuela's history. He was removed from power by military officers in the 1948 Venezuelan coup.

Rómulo Gallegos is considered the most relevant Venezuelan novelist of the 20th century, and a prominent figure in Latin American literature.
Question: write a linkedin informal message asking to an existing company employee for tips and guidelines for the specific role in their company. It should not be direct. Also ask about there experience or journey. and how to prepare to crack the exam and interview. It should be short.
Answer: Good Evening [Referrer],
Can you please guide me and share your experience of your Software Engineer Intern Journey at [Company].
And how to prepare for the online tests and technical interviews. Anything specific which can help us to prepare for the same. It will be beneficial to prepare in short span of time.
Question: What kind of vector is used to initialize their embedding matrix?
Evidence: Common to all the models, we initialize our embedding matrix with pre-trained Glove word vectors of 300 dimensions using 6B tokens . Out of 11319 tokens present in the dataset, we found 188 tokens missing from the pre-trained Glove embeddings, so we manually map these tokens to words conveying semantically similar meaning, e.g. we map over ten variations of the word “yes” - misspelled or not picked up by tokenizer - “*yes", “yesa", “yess", “ytes", “yes-", “yes3", “yyes", “yees", etc.
For image features, we extract Faster R-CNN features with ResNet-101 backbone trained on Visual genome BIBREF4 dataset, similar to BIBREF2. We use an adaptive number of object proposals per-image ranging from 10 to 100 generated using a fixed confidence threshold and each object is then associated with 2048-dimensional mean-pooled features using ROI pooling. We use discriminative decoding throughout our models.
We first describe our models individually and then the ensembling technique that we employ. In the following, MN denotes Memory Networks to encode conversational history, RCNN signify R-CNN for object level representations of an image, Wt represents additional linear layer in the decoder, and LF a late fusion mechanism as defined in BIBREF0.
Common to all the models, we initialize our embedding matrix with pre-trained Glove word vectors of 300 dimensions using 6B tokens 1.
Answer: They initialize their embedding matrix with pre-trained Glove word vectors of 300 dimensions using 6B tokens.
Question: What gaps does this study fill？
Evidence: Grapheme-to-phoneme conversion (g2p) is necessary for text-to-speech and automatic speech recognition systems. Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling--pronunciation pairs in hundreds of languages. The system shares a single encoder and decoder across all languages, allowing it to utilize the intrinsic similarities between different writing systems. We show an 11% improvement in phoneme error rate over an approach based on adapting high-resource monolingual g2p models to low-resource languages. Our model is also much more compact relative to previous approaches.
Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, we present a neural sequence-to-sequence approach to g2p which is trained on spelling-pronunciation pairs in hundreds of languages.
Answer: Most g2p systems are monolingual: they require language-specific data or handcrafting of rules. Such systems are difficult to extend to low resource languages, for which data and handcrafted rules are not available. As an alternative, they present a neural sequence-to-sequence approach to g2p which is trained on spelling-pronunciation pairs in hundreds of languages.
Question: What are the methods they actually provide?
Evidence: Following developing news stories is imperative to making real-time decisions on important political and public safety matters. Given the abundance of media providers and languages, this endeavor is an extremely difficult task. As such, there is a strong demand for automatic clustering of news streams, so that they can be organized into stories or themes for further processing. Performing this task in an online and efficient manner is a challenging problem, not only for newswire, but also for scientific articles, online reviews, forum posts, blogs, and microblogs.
A key challenge in handling document streams is that the story clusters must be generated on the fly in an online fashion: this requires handling documents one-by-one as they appear in the document stream. In this paper, we provide a treatment to the problem of online document clustering, i.e. the task of clustering a stream of documents into themes. For example, for news articles, we would want to cluster them into related news stories.
To this end, we introduce a system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream. Our clustering approach is part of a larger media monitoring project to solve the problem of monitoring massive text and TV/Radio streams (speech-to-text). In particular, media monitors write intelligence reports about the most relevant events, and being able to search, visualize and explore news clusters assists in gathering more insight about a particular story. Since relevant events may be spawned from any part of the world (and from many multilingual sources), it becomes imperative to cluster news across different languages.
In terms of granularity, the type of story clusters we are interested in are the group of articles which, for example : (i) Narrate recent air-strikes in Eastern Ghouta (Syria); (ii) Describe the recent launch of Space X's Falcon Heavy rocket.
In this paper, we provide a treatment to the problem of online document clustering, i.e. the task of clustering a stream of documents into themes.
Answer: The task of clustering a stream of documents into themes.
Question: What is the best drum kit?
Answer: There are many different manufacturers of drum kits, with a wide range of models and pricing. Some of the most popular brands include DW, Tama, Sonor, Mapex, Pearl, Yamaha, Ludwig, Gretsch and the British Drum Company. There are also excellent custom drum builders with high quality products such as SJC and A&F.
The majority of these brands offer kits for beginners all the way up to touring and studio professionals.
Question: Which models were compared?
Evidence: For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 . KIM is particularly interesting in this context as it performed significantly better than other models in the Breaking NLI experiment conducted by BIBREF1 . The success of pre-trained language models in multiple NLP tasks make ESIM + ELMo and BERT interesting additions to this experiment. Table 3 lists the different models used in the experiments.
For sentence encoding models, we chose a simple one-layer bidirectional LSTM with max pooling (BiLSTM-max) with the hidden size of 600D per direction, used e.g. in InferSent BIBREF17 , and HBMP BIBREF18 . For the other models, we have chosen ESIM BIBREF19 , which includes cross-sentence attention, and KIM BIBREF2 , which has cross-sentence attention and utilizes external knowledge. We also selected two model involving a pre-trained language model, namely ESIM + ELMo BIBREF20 and BERT BIBREF0 .
Answer: BiLSTM-max, HBMP, ESIM, KIM, ESIM + ELMo, and BERT
Question: Based on this paragraph about a singer, where was Shakira born?
Evidence: Shakira Isabel Mebarak Ripoll (/ʃəˈkɪərə/ shə-KEER-ə, Spanish: [(t)ʃaˈkiɾa]; born 2 February 1977) is a Colombian singer and songwriter. Born and raised in Barranquilla, she has been referred to as the "Queen of Latin Music" and is noted for her musical versatility. She made her recording debut with Sony Music Colombia at the age of 13. Following the commercial failure of her first two albums, Magia (1991) and Peligro (1993), she rose to prominence in Hispanic countries with her next albums, Pies Descalzos (1995) and Dónde Están los Ladrones? (1998). She entered the English-language market with her fifth album, Laundry Service (2001), which sold over 13 million copies worldwide. Buoyed by the international success of her singles "Whenever, Wherever" and "Underneath Your Clothes", the album propelled her reputation as a leading crossover artist. Broadcast Music, Inc., described Shakira as a "pioneer" who extended the global reach of Latino singers.
Answer: Barranquilla, Colombia
Question: What has been found out of 11319 tokens present in the dataset?
Evidence: Visual dialog BIBREF0 is an interesting new task combining the research efforts from Computer Vision, Natural Language Processing and Information Retrieval. While BIBREF1 presents some tips and tricks for VQA 2.0 Challenge, we follow their guidelines for the Visual Dialog challenge 2018. Our models use attention similar to BIBREF2 to get object level image representations from Faster R-CNN model BIBREF3. We experiment with different encoder mechanisms to get representations of conversational history.
Out of 11319 tokens present in the dataset, we found 188 tokens missing from the pre-trained Glove embeddings, so we manually map these tokens to words conveying semantically similar meaning,
Answer: 188 tokens missing from the pre-trained Glove embeddings.
Question: Which metrics do they use to evaluate simultaneous translation?
Evidence: We used “Wait-k” models and general NMT models as baseline models. General NMT models were attention-based encoder-decoder and it translated sentences from full-length source sentences (called Full Sentence). For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy.
For evaluation metrics, we used BLEU BIBREF8 and RIBES BIBREF9 to measure translation accuracy, and token-level delay to measure latency. We used Kytea BIBREF10 as a tokenize method for evaluations of Japanese translation accuracy.
Answer: The answers are shown as follows:
* BLEU BIBREF8
* RIBES BIBREF9
* token-level delay
Question: What are the primary benefits of sending my child to daycare?
Answer: There are many benefits to sending your child to daycare. Daycare centers provide an effective way to socialize your child with other children and adult teachers. This allows your child to form relationships and build trust with people outside of their core family unit. Daycares provide structured learning activities that are optimized for the child’s current development stage, assisting their intellectual growth by sparking neural connections. Daycares also benefit the parents, as the professionally-trained teachers provide advice and guidance on how the parents can employ effective methods to guide their children’s development. There are many benefits to sending your child to daycare, and ultimately the decision depends on what you feel is best for your child’s development.
Question: What are the biggest marathons?
Answer: There are hundreds of marathons around the globe every year.  Only 6 marathons are designated as the major marathons.  Those include the Tokyo Marathon, Boston Marathon, London Marathon, Berlin Marathon, Chicago Marathon and New York City Marathon.