DataHammer/scimrc huggingface dataset:
Question: How does the gatint mechanism combine word and character information?
Evidence: Using the fine-grained gating mechanism conditioned on the lexical features, we can accurately control the information flows between word-level and char-level. Intuitively, the formulation is as follows: INLINEFORM0
where INLINEFORM0 is the element-wise multiplication operator. when the gate has high value, more information flows from the word-level representation; otherwise, char-level will take the dominating place. It is practical in real scenarios. For example, for unfamiliar noun entities, the gates tend to bias towards char-level representation in order to care richer morphological structure. Besides, we not only utilize the lexical properties as the gating feature, we also concatenate them as a supplement of lexical information. Therefore, the final representation of words are computed as follows: INLINEFORM1
Using the fine-grained gating mechanism conditioned on the lexical features, we can accurately control the information flows between word-level and char-level. Intuitively, the formulation is as follows: INLINEFORM0

where INLINEFORM0 is the element-wise multiplication operator. when the gate has high value, more information flows from the word-level representation; otherwise, char-level will take the dominating place. It is practical in real scenarios. For example, for unfamiliar noun entities, the gates tend to bias towards char-level representation in order to care richer morphological structure.
Answer: The answers are shown as follows:
* when the gate has high value, more information flows from the word-level representation; otherwise, char-level will take the dominating place
*  for unfamiliar noun entities, the gates tend to bias towards char-level representation in order to care richer morphological structure
Question: Provide three bullets about Einstein from this paragraph that does not relate to physics
Evidence: Albert Einstein (14 March 1879 – 18 April 1955) was a German-born theoretical physicist, widely acknowledged to be one of the greatest and most influential physicists of all time. Einstein is best known for developing the theory of relativity, but he also made important contributions to the development of the theory of quantum mechanics. Relativity and quantum mechanics are the two pillars of modern physics. His mass–energy equivalence formula E = mc2, which arises from relativity theory, has been dubbed "the world's most famous equation". His work is also known for its influence on the philosophy of science. He received the 1921 Nobel Prize in Physics "for his services to theoretical physics, and especially for his discovery of the law of the photoelectric effect", a pivotal step in the development of quantum theory. His intellectual achievements and originality resulted in "Einstein" becoming synonymous with "genius". Einsteinium, one of the synthetic elements in the periodic table, was named in his honor.
Answer: - Einstein was born in Germany
- Einstein lived to 76 years old
- One part of Einstein’s legacy was that his name became synonymous with “genius”
Question: Where is Peloton Interactive based?
Evidence: Peloton Interactive, Inc. is an American exercise equipment and media company based in New York City. The company's products are stationary bicycles, treadmills, and indoor rowers equipped with Internet-connected touch screens that stream live and on-demand fitness classes through a subscription service. The equipment includes built-in sensors that track metrics such as power output, providing users with real-time feedback on their performance and leaderboard rankings to compete with other users.
Answer: Peloton Interactive is based in New York City, NY, US.  They are an American exercise and media company.  Their products include stationary spin bicycles, treadmills, and rowers.  Their products feature internet-connected touch screens to stream live and on-demand fitness classes.  Sensors in the equipment track metrics such as power, cadence, and resistance to provide real-time feedback to the user.  Users must subscribe to Peloton Interactive services to use classes on the equipment.
Question: When was the NFL founded? Which NFL team won the most Super Bowls? Which quarterback has won the most Super Bowls?
Answer: The NFL was officially founded in 1920.
The New England Patriots have won 6 titles.
Tom Brady has won 7 superbowls
Question: Who gave the slogan of Quit India and Simon Go Back?
Answer: Yusuf Meherally
Question: Tell me whether you eat each of these with a spoon or fork: soup, ice cream, cereal, salad, pasta
Answer: Soup: Spoon
Ice Cream: Spoon
Cereal: Spoon
Salad: Fork
Pasta: Fork
Question: How does the model circumvent the lack of supporting facts during training?
Evidence: We have proposed new modules for the DMN framework to achieve strong results without supervision of supporting facts. These improvements include the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs. Our resulting model obtains state of the art results on both the VQA dataset and the bAbI-10k text question-answering dataset, proving the framework can be generalized across input domains.
 the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs.
Answer: The answers are shown as follows:
* the input fusion layer to allow interactions between input facts and a novel attention based GRU that allows for logical reasoning over ordered inputs.
Question: Given a reference text about twitter, how many accounts were estimated to be fake?
Evidence: Twitter was created by Jack Dorsey, Noah Glass, Biz Stone, and Evan Williams in March 2006 and launched in July of that year. Twitter, Inc. is based in San Francisco, California and has more than 25 offices around the world. By 2012, more than 100 million users tweeted 340 million tweets a day, and the service handled an average of 1.6 billion search queries per day. In 2013, it was one of the ten most-visited websites and has been described as "the SMS of the Internet". By the start of 2019, Twitter had more than 330 million monthly active users. In practice, the vast majority of tweets are tweeted by a minority of users. In 2020, it was estimated that approximately 48 million accounts (15% of all accounts) were fake.
Answer: In 2020, about 48 million accounts were estimated to be fake.
Question: Which architecture performs best they find?
Evidence: Named entity typing is the task of detecting the type (e.g., person, location, or organization) of a named entity in natural language text. Entity type information has shown to be useful in natural language tasks such as question answering BIBREF0 , knowledge-base population BIBREF1 , BIBREF2 , and co-reference resolution BIBREF3 . Motivated by its application to downstream tasks, recent work on entity typing has moved beyond standard coarse types towards finer-grained semantic types with richer ontologies BIBREF0 , BIBREF4 , BIBREF5 , BIBREF6 , BIBREF7 . Rather than assuming an entity can be uniquely categorized into a single type, the task has been approached as a multi-label classification problem: e.g., in “... became a top seller ... Monopoly is played in 114 countries. ...” (fig:arch), “Monopoly” is considered both a game as well as a product.
The state-of-the-art approach BIBREF8 for fine-grained entity typing employs an attentive neural architecture to learn representations of the entity mention as well as its context. These representations are then combined with hand-crafted features (e.g., lexical and syntactic features), and fed into a linear classifier with a fixed threshold. While this approach outperforms previous approaches which only use sparse binary features BIBREF4 , BIBREF6 or distributed representations BIBREF9 , it has a few drawbacks: (1) the representations of left and right contexts are learnt independently, ignoring their mutual connection; (2) the attention on context is computed solely upon the context, considering no alignment to the entity; (3) document-level contexts which could be useful in classification are not exploited; and (4) hand-crafted features heavily rely on system or human annotations.
To overcome these drawbacks, we propose a neural architecture (fig:arch) which learns more context-aware representations by using a better attention mechanism and taking advantage of semantic discourse information available in both the document as well as sentence-level contexts. Further, we find that adaptive classification thresholds leads to further improvements. Experiments demonstrate that our approach, without any reliance on hand-crafted features, outperforms prior work on three benchmark datasets.
We found that a flat architecture with inter-sentence Transformer layers performs the best, achieving the state-of-the-art results on this task.
Answer: A flat architecture with inter-sentence Transformer layers.
Question: On what model architectures are previous co-occurence networks based?
Evidence: In a more practical scenario, text networks have been used in text classification tasks BIBREF8, BIBREF9, BIBREF10. The main advantage of the model is that it does not rely on deep semantical information to obtain competitive results. Another advantage of graph-based approaches is that, when combined with other approaches, it yields competitive results BIBREF11. A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.
A simple, yet recurrent text model is the well-known word co-occurrence network. After optional textual pre-processing steps, in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window. A common strategy connects only adjacent words in the so called word adjacency networks.
Answer: The answers are shown as follows:
* in a co-occurrence network each different word becomes a node and edges are established via co-occurrence in a desired window
* connects only adjacent words in the so called word adjacency networks
Question: Who named the Landscape Arch?
Evidence: The arch is among many in the Devils Garden area in the north of the park. Landscape Arch was named by Frank Beckwith who explored the area in the winter of 1933–1934 as the leader of an Arches National Monument scientific expedition. The arch can be reached by a 0.8 mi (1.3 km) graded gravel trail.
Answer: The landscape arch was named by Frank Beckwith who found the arch by exploring the area in the winter of 1933-1934
Question: What kinds of neural networks did they use in this paper?
Evidence: For NMT, we used the KyotoNMT system BIBREF16 . The NMT training settings are the same as those of the best systems that participated in WAT 2016. The sizes of the source and target vocabularies, the source and target side embeddings, the hidden states, the attention mechanism hidden states, and the deep softmax output with a 2-maxout layer were set to 32,000, 620, 1000, 1000, and 500, respectively. We used 2-layer LSTMs for both the source and target sides. ADAM was used as the learning algorithm, with a dropout rate of 20% for the inter-layer dropout, and L2 regularization with a weight decay coefficient of 1e-6. The mini batch size was 64, and sentences longer than 80 tokens were discarded. We early stopped the training process when we observed that the BLEU score of the development set converges. For testing, we self ensembled the three parameters of the best development loss, the best development BLEU, and the final parameters. Beam size was set to 100.
For NMT, we used the KyotoNMT system BIBREF16 . The NMT training settings are the same as those of the best systems that participated in WAT 2016. The sizes of the source and target vocabularies, the source and target side embeddings, the hidden states, the attention mechanism hidden states, and the deep softmax output with a 2-maxout layer were set to 32,000, 620, 1000, 1000, and 500, respectively. We used 2-layer LSTMs for both the source and target sides. ADAM was used as the learning algorithm, with a dropout rate of 20% for the inter-layer dropout, and L2 regularization with a weight decay coefficient of 1e-6. The mini batch size was 64, and sentences longer than 80 tokens were discarded.
Answer: The answers are shown as follows:
* LSTMs
Question: Do Jasper models contain an additional pre-processing block?
Evidence: Conventional automatic speech recognition (ASR) systems typically consist of several independently learned components: an acoustic model to predict context-dependent sub-phoneme states (senones) from audio, a graph structure to map senones to phonemes, and a pronunciation model to map phonemes to words. Hybrid systems combine hidden Markov models to model state dependencies with neural networks to predict states BIBREF0 , BIBREF1 , BIBREF2 , BIBREF3 . Newer approaches such as end-to-end (E2E) systems reduce the overall complexity of the final system.
Our research builds on prior work that has explored using time-delay neural networks (TDNN), other forms of convolutional neural networks, and Connectionist Temporal Classification (CTC) loss BIBREF4 , BIBREF5 , BIBREF6 . We took inspiration from wav2letter BIBREF6 , which uses 1D-convolution layers. Liptchinsky et al. BIBREF7 improved wav2letter by increasing the model depth to 19 convolutional layers and adding Gated Linear Units (GLU) BIBREF8 , weight normalization BIBREF9 and dropout.
By building a deeper and larger capacity network, we aim to demonstrate that we can match or outperform non end-to-end models on the LibriSpeech and 2000hr Fisher+Switchboard tasks. Like wav2letter, our architecture, Jasper, uses a stack of 1D-convolution layers, but with ReLU and batch normalization BIBREF10 . We find that ReLU and batch normalization outperform other activation and normalization schemes that we tested for convolutional ASR. As a result, Jasper's architecture contains only 1D convolution, batch normalization, ReLU, and dropout layers – operators highly optimized for training and inference on GPUs.
It is possible to increase the capacity of the Jasper model by stacking these operations. Our largest version uses 54 convolutional layers (333M parameters), while our small model uses 34 (201M parameters). We use residual connections to enable this level of depth. We investigate a number of residual options and propose a new residual connection topology we call Dense Residual (DR).
Integrating our best acoustic model with a Transformer-XL BIBREF11 language model allows us to obtain new state-of-the-art (SOTA) results on LibriSpeech BIBREF12 test-clean of 2.95% WER and SOTA results among end-to-end models on LibriSpeech test-other. We show competitive results on Wall Street Journal (WSJ), and 2000hr Fisher+Switchboard (F+S). Using only greedy decoding without a language model we achieve 3.86% WER on LibriSpeech test-clean.
This paper makes the following contributions:
All Jasper models have four additional convolutional blocks: one pre-processing and three post-processing.
Answer: Yes.
Question: When did Videotron withdraw from the Cable Television Standards Council (CTSC)?
Evidence: The Cable Television Standards Council of Canada was an independent organization established by the Canadian cable television industry to administer Standards, Codes and Guidelines that ensure high standards of customer service. In February 2002, Vidéotron withdrew from the CTSC . Later that year, effective December 1, Shaw also withdrew . "The CTSC dealt with complaints with respect to cable service, such as concerns about quality of service and billing until 12 April 2006, when this organisation ceased operation. Since then, complaints filed with the Commission about quality of service and billing are forwarded to the appropriate licensee [that is a cable company] for resolution." 
Answer: Videotron withdrew from the Cable Television Standards Council in February 2002.
Question: which tasks are used in BLUE benchmark?
Evidence: FLOAT SELECTED: Table 1: BLUE tasks
FLOAT SELECTED: Table 1: BLUE tasks
Answer: The answers are shown as follows:
* Inference task
The aim of the inference task is to predict whether the premise sentence entails or contradicts the hypothesis sentence
* Document multilabel classification
The multilabel classification task predicts multiple labels from the texts.
* Relation extraction
The aim of the relation extraction task is to predict relations and their types between the two entities mentioned in the sentences.
* Named entity recognition
The aim of the named entity recognition task is to predict mention spans given in the text 
* Sentence similarity
The sentence similarity task is to predict similarity scores based on sentence pairs
Question: What are 3 popular movies with Adam Sandler?
Answer: The following are 3 popular movies with Adam Sandler:
1. Big Daddy
2. Billy Madison
3. The Waterboy
Question: How many fully connected layers with how many neurons were used?
Evidence: The fully-connected layers consist of three layers and the number of the units are 512, 256, and 1 respectively
Answer: We added the description of the fully-connected layer.
Question: What was the primary reason for constructing the Berlin Wall?
Evidence: The Berlin Wall (German: Berliner Mauer, pronounced [bɛʁˌliːnɐ ˈmaʊ̯ɐ] (listen)) was a guarded concrete barrier that encircled West Berlin of the Federal Republic of Germany (FRG) from 1961 to 1989, separating it from East Berlin and the German Democratic Republic (GDR) (GDR). Construction of the Berlin Wall was commenced by the government of the GDR on 13 August 1961. It included guard towers placed along large concrete walls, accompanied by a wide area (later known as the "death strip") that contained anti-vehicle trenches, beds of nails and other defenses. The primary intention for the Wall's construction was to prevent East German citizens from fleeing to the West.
Answer: The Berlin Wall was primarily constructed to prevent East German citizens from fleeing to the West.
Question: What is the YIMBY movement?
Answer: YIMBY stands for Yes In My Back Yard. The YIMBY movement is a grassroots response to the phenomenon of NIMBYism, which stands for Not In My Back Yard, and is the name for concentrated local opposition to societally-beneficial resources. For example, NIMBY neighbors might oppose the construction of a new hospital in their neighborhood on the grounds that it will increase local traffic. The whole city will benefit from the new hospital, but the immediate neighbors will oppose it on these grounds regardless of where in the city it is constructed.

The YIMBY movement in the United States is primarily concerned with housing. YIMBY organizations have been formed across the country to support measures that will increase the supply of housing, such as repealing bans on the construction of apartment buildings.
Question: Can this approach model n-ary relations?
Evidence: For the future work, we plan to solve the triples with multiple entities as the second entity, which is excluded from problem scope in this paper. Besides, processing longer documents and improving the quality of our benchmark are all challenging problems as we mentioned previously. We hope this work can provide new thoughts for the area of information extraction.
The input of QA4IE is a document $D$ with an existing knowledge base $K$ and the output is a set of relation triples $R = \lbrace e_i, r_{ij}, e_j\rbrace $ in $D$ where $e_i$ and $e_j$ are two individual entities and $r_{ij}$ is their relation. We ignore the adverbials and only consider the entity pairs and their relations as in standard RE settings. Note that we process the entire document as a whole instead of processing individual sentences separately as in previous systems. As shown in Figure 1 , our QA4IE framework consists of four key steps:
For the future work, we plan to solve the triples with multiple entities as the second entity, which is excluded from problem scope in this paper.
The input of QA4IE is a document $D$ with an existing knowledge base $K$ and the output is a set of relation triples $R = \lbrace e_i, r_{ij}, e_j\rbrace $ in $D$ where $e_i$ and $e_j$ are two individual entities and $r_{ij}$ is their relation.
Answer: No.
Question: What effectiveness of language model rescoring do they study?
Evidence: Machine translation (MT) has achieved huge advances in the past few years BIBREF1, BIBREF2, BIBREF3, BIBREF4. However, the need for a large amount of manual parallel data obstructs its performance under low-resource conditions. Building an effective model on low resource data or even in an unsupervised way is always an interesting and challenging research topic BIBREF5, BIBREF6, BIBREF7. Recently, unsupervised MT BIBREF8, BIBREF9, BIBREF0, BIBREF10, BIBREF11, which can immensely reduce the reliance on parallel corpora, has been gaining more and more interest.
Training cross-lingual word embeddings BIBREF0, BIBREF12 is always the first step of the unsupervised MT models which produce a word-level shared embedding space for both the source and target, but the lexical coverage can be an intractable problem. To tackle this issue, BIBREF13 provided a subword-level solution to overcome the out-of-vocabulary (OOV) problem.
In this work, the systems we implement for the German-Czech language pair are built based on the previously proposed unsupervised MT systems, with some adaptations made to accommodate the morphologically rich characteristics of German and Czech BIBREF14. Both word-level and subword-level neural machine translation (NMT) models are applied in this task and further tuned by pseudo-parallel data generated from a phrase-based statistical machine translation (PBSMT) model, which is trained following the steps proposed in BIBREF10 without using any parallel data. We propose to train BPE embeddings for German and Czech separately and align those trained embeddings into a shared space with MUSE BIBREF0 to reduce the combinatorial explosion of word forms for both languages. To ensure the fluency and consistency of translations, an additional Czech language model is trained to select the translation candidates generated through beam search by rescoring them. Besides the above, a series of post-processing steps are applied to improve the quality of final translations. Our contribution is two-fold:
We propose a method to combine word and subword (BPE) pre-trained input representations aligned using MUSE BIBREF0 as an NMT training initialization on a morphologically-rich language pair such as German and Czech.
We study the effectiveness of language model rescoring to choose the best sentences and unknown word replacement (UWR) procedure to reduce the drawback of OOV words.
This paper is organized as follows: in Section SECREF2, we describe our approach to the unsupervised translation from German to Czech. Section SECREF3 reports the training details and the results for each steps of our approach. More related work is provided in Section SECREF4. Finally, we conclude our work in Section SECREF5.
We study the effectiveness of language model rescoring to choose the best sentences and unknown word replacement (UWR) procedure to reduce the drawback of OOV words.
Answer: To choose the best sentences and unknown word replacement (UWR) procedure to reduce the drawback of OOV words.
Question: Give me the list of top 10 movies of all time, by IMDB rating. Format: Title (Release Date) Rating
Answer: These are the top 10 movies based on their IMDB rating:

1. The Shawshank Redemption (1994)  9.2     
2. The Godfather (1972) 9.2     
3. The Dark Knight (2008)   9.0     
4. The Godfather Part II (1974) 9.0     
5. 12 Angry Men (1957)  9.0     
6. Schindler's List (1993)  8.9     
7. The Lord of the Rings: The Return of the King (2003) 8.9     
8. Pulp Fiction (1994)  8.8     
9. The Lord of the Rings: The Fellowship of the Ring (2001) 8.8     
10. The Good, the Bad and the Ugly (1966)   8.8