DataHammer/scimrc huggingface dataset:
Question: The authors experimentally show that their new architecture leads to what when extending the Sequence2Sequence model?
Evidence: Neural Networks have proven to be useful for automating tasks such as question answering, system response, and language generation considering large textual datasets. In learning systems, bias can be defined as the negative consequences derived by the implicit association of patterns that occur in a high-dimensional space. In dialogue systems, these patterns represent associations between word embeddings that can be measured by a Cosine distance to observe male- and female-related analogies that resemble the gender stereotypes of the real world. We propose an automatic technique to mitigate bias in language generation models based on the use of an external memory in which word embeddings are associated to gender information, and they can be sparsely updated based on content-based lookup.
The main contributions of our work are the following:
We introduce a novel architecture that considers the notion of a Fair Region to update a subset of the trainable parameters of a Memory Network.
We experimentally show that this architecture leads to mitigate gender bias amplification in the automatic generation of text when extending the Sequence2Sequence model.
We introduce a novel architecture that considers the notion of a Fair Region to update a subset of the trainable parameters of a Memory Network. We experimentally show that this architecture leads to mitigate gender bias amplification in the automatic generation of text when extending the Sequence2Sequence model.
Answer: Mitigate gender bias amplification in the automatic generation of text.
Question: Does the model proposed by the authors outperform previous works?
Evidence: Conflating hatred content with offensive or harmless language causes online automatic hate speech detection tools to flag user-generated content incorrectly. Not addressing this problem may bring about severe negative consequences for both platforms and users such as decreasement of platforms' reputation or users abandonment. Here, we propose a transfer learning approach advantaging the pre-trained language model BERT to enhance the performance of a hate speech detection system and to generalize it to new datasets. To that end, we introduce new fine-tuning strategies to examine the effect of different layers of BERT in hate speech detection task. The evaluation results indicate that our model outperforms previous works by profiting the syntactical and contextual information embedded in different transformer encoder layers of the BERT model using a CNN-based fine-tuning strategy. Furthermore, examining the results shows the ability of our model to detect some biases in the process of collecting or annotating datasets. It can be a valuable clue in using the pre-trained BERT model to alleviate bias in hate speech datasets in future studies, by investigating a mixture of contextual information embedded in the BERT’s layers and a set of features associated to the different type of biases in data.

The evaluation results indicate that our model outperforms previous works by profiting the syntactical and contextual information embedded in different transformer encoder layers of the BERT model using a CNN-based fine-tuning strategy.
Answer: Yes, it does.
Question: What did the authors employ to test the clusters?
Evidence: To test our clusters, we employed the Word Intrusion task BIBREF35. We present the annotator with five words – four drawn from one cluster and one drawn randomly from the domain vocabulary – and we ask them to pick out the intruder. The intuition is that if the cluster is coherent, then an observer should be able to identify the out-of-cluster word as the intruder. For both domains, we report results on all clusters and on the top 8, ranked by ascending normalized sum of squared errors, which can be seen as a prediction of coherence. In the celebrity domain, annotators identified the out-of-cluster word 73% of the time in the top-8 and 53% overall. In the professor domain, annotators identified it 60% of the time in the top-8 and 49% overall. As expected, top-8 performance in both domains does considerably better than overall, but at all levels the precision is significantly above the random baseline of 20%.
To test cluster labels, we present the annotator with a label and a word, and we ask them whether the word falls under the concept. The concept is a potential cluster label and the word is either a word from that cluster or drawn randomly from the domain vocabulary. For a good label, the rate at which in-cluster words fall under the label should be much higher than the rate at which out-of-cluster words fall under. In our experiments, we tested the top 4 predicted labels and the centroid of the cluster as a strong baseline label. The centroid achieved an in-cluster rate of .60 and out-of-cluster rate of .18 (difference of .42). Our best performing predicted label achieved an in-cluster rate of .65 and an out-of-cluster rate of .04 (difference of .61), thus outperforming the centroid on both rates and increasing the gap between rates by nearly 20 points. In the Appendix, we include more detailed results on both tasks.
To test our clusters, we employed the Word Intrusion task (Chang et al., 2009).
Answer: To test their clusters, the authors employed the Word Intrusion task.
Question: Select the odd word from this list and explain why you chose it.
Larch
Elm
Oak
Yew
Rhododendron
Answer: Rhododendron is the odd word from this list. Larch, Elm, Oak and Yew are all examples of trees, whereas the Rhododendron is a flower.
Question: Who were the children of Bill 101
Evidence: The children of Bill 101 (les enfants de la loi 101) is the name given to the generation of children whose parents immigrated to Quebec, Canada after the adoption of the 1977 Charter of the French Language (Bill 101).

One of the Charter's articles stipulates that all children under 16 must receive their primary and secondary education in French schools, unless one of the child's parents has received most of their education in English, in Canada, or the child or the child's sibling has already received a substantial part of their education in English, in Canada. If a child qualifies for a certificate of eligibility for instruction in English under one of those criteria, then the certificate is permanent and can be passed on to future generations even if the child ends up attending French instead of English school. The permanent certificate can only be granted if one of the child's parents is a Canadian citizen. In addition, children of foreign temporary residents living in Quebec on study or work permits, as well as children of diplomats, can also obtain a temporary certificate of eligibility that is only valid for the duration of their permit.
Answer: The children of Bill 101 (les enfants de la loi 101) is the name given to the generation of children whose parents immigrated to Quebec, Canada after the adoption of the 1977 Charter of the French Language (Bill 101).

One of the Charter's articles stipulates that all children under 16 must receive their primary and secondary education in French schools, unless one of the child's parents has received most of their education in English, in Canada, or the child or the child's sibling has already received a substantial part of their education in English, in Canada.
Question: Why questions boost creativity during brainstorming sessions
Answer: A compelling, well-timed question can activate lively discussions and unexpected solutions. A poor question meant as a shortcut to a solution can often get you blank stares and an awkward silence. We’ve all been there!

That’s why it’s important that team leaders utilize and prepare creative brainstorming questions that encourage free thinking, creativity, and, yes, even more questions from your team. At face value, facilitating a conversation that creates even more questions might seem like it could lead to wasted time, but it’s actually an effective way to help your team invest in solving the problem at hand and engage in dynamic solutions as a group. 

Below we cover a wide range of brainstorming questions team leaders can use.
Question: Why are some reasons to vacation in New Zealand?
Answer: People love to vacation in New Zealand for many reasons.  Some of the most popular are  because of its beautiful and varied landscapes, the opportunities to see unique wildlife, and the ability to participate in extreme activities such as paragliding, jet boating, bungee jumping, sky diving, canyoning, and mountaineering.
Question: What did the experiment conclude?
Evidence: We have proposed a novel RvNN architecture to fully utilize linguistic priors. A newly introduced tag-level tree-LSTM demonstrates that it can effectively control the composition function of the corresponding word-level tree-LSTM. In addition, the proper contextualization of the input word vectors results in significant performance improvements on several sentence-level tasks. For future work, we plan to explore a new way of exploiting dependency trees effectively, similar to the case of constituency trees.
A newly introduced tag-level tree-LSTM demonstrates that it can effectively control the composition function of the corresponding word-level tree-LSTM. In addition, the proper contextualization of the input word vectors results in significant performance improvements on several sentence-level tasks.
Answer: A newly introduced tag-level tree-LSTM demonstrates that it can effectively control the composition function of the corresponding word-level tree-LSTM. In addition, the proper contextualization of the input word vectors results in significant performance improvements on several sentence-level tasks.
Question: Which USA Women's world cup player famously celebrated a goal by miming a tea sip?
Answer: Alex Morgan
Question: What is an adjacency list?
Evidence: In graph theory and computer science, an adjacency list is a collection of unordered lists used to represent a finite graph. Each unordered list within an adjacency list describes the set of neighbors of a particular vertex in the graph. This is one of several commonly used representations of graphs for use in computer programs.
Answer: In graph theory and computer science, an adjacency list is a collection of unordered lists used to represent a finite graph.
Question: How do they confirm their model working well on out-of-vocabulary problems?
Evidence: To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.
To test the robustness of the taggers against the OOV problem, we also conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set. Again, the CNN tagger outperforms the two baselines by a very large margin.
Answer: The answers are shown as follows:
* conduct experiments using artificially constructed unnormalized text by corrupting words in the normal dev set
Question: What is their benchmark?
Evidence: To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of Rouge variants and our metric (Sera), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries.
To the best of our knowledge, the only scientific summarization benchmark is from TAC 2014 summarization track. For evaluating the effectiveness of ROUGE variants and our metric (SERA), we use this benchmark, which consists of 20 topics each with a biomedical journal article and 4 gold human written summaries.
Answer: TAC 2014 summarization track.
Question: What is Jasper Dense Residual?
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:
We also build a variant of Jasper, Jasper Dense Residual (DR).
Answer: It is a variant of Jasper.
Question: What does a lawyer do?
Answer: A lawyer helps to interpret laws and rules for business, and individuals.  If needed they will help their clients execute a lawsuit in court on their behalf by presenting facts verbally and written.  If they are not going to court they can also help with legal issues and documentation, contracts, wills,  and appeals.  An example of a lawyer is a patent or divorce lawyer.  Another name for a lawyer is an attorney.
Question: Identify which instrument is string or percussion: Maddale, Banhu
Answer: Banhu is string, Maddale is percussion.
Question: What is solar wind?
Answer: Solar wind is a force that results from discharged light and gas particles from stars.
Question: Which is the capital of Union Territory of Ladakh ?
Evidence: The Leh–Manali Highway is a 428 km (266 mi) long highway in northernmost India connecting Leh, the capital of the Union Territory of Ladakh, to Manali in the state of Himachal Pradesh.
Answer: Leh is the capital of the Union Territory of Ladakh.
Question: Do the BiLSTMs perform best as expected?
Evidence: The dataset was thoroughly evaluated, see leitner2019fine for more details. As state of the art models, Conditional Random Fields (CRFs) and bidirectional Long-Short Term Memory Networks (BiLSTMs) were tested with the two variants of annotation. For CRFs, these are: CRF-F (with features), CRF-FG (with features and gazetteers), CRF-FGL (with features, gazetteers and lookup). For BiLSTM, we used models with pre-trained word embeddings BIBREF22: BiLSTM-CRF BIBREF23, BiLSTM-CRF+ with character embeddings from BiLSTM BIBREF24, and BiLSTM-CNN-CRF with character embeddings from CNN BIBREF25. To evaluate the performance we used stratified 10-fold cross-validation. As expected, BiLSTMs perform best (see Table ). The F$_1$ score for the fine-grained classification reaches 95.46 and 95.95 for the coarse-grained one. CRFs reach up to 93.23 F$_1$ for the fine-grained classes and 93.22 F$_1$ for the coarse-grained ones. Both models perform best for judge, court and law.
As expected, BiLSTMs perform best (see Table 4).
Answer: Yes, they do.
Question: What is the disadvantage of VAEs?
Evidence: Our approach is related to flow-based generative models, which are first described in NICE BIBREF16 and have recently received more attention BIBREF17 , BIBREF19 , BIBREF18 . This relevant work mostly adopts simple (e.g. Gaussian) and fixed priors and does not attempt to learn interpretable latent structures. Another related generative model class is variational auto-encoders (VAEs) BIBREF45 that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures BIBREF46 , BIBREF47 . Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges BIBREF48 . Our approach can also be viewed in connection with generative adversarial networks (GANs) BIBREF49 that is a likelihood-free framework to learn implicit generative models. However, it is non-trivial for a gradient-based method like GANs to propagate gradients through discrete structures.
Another related generative model class is variational auto-encoders (VAEs) (Kingma and Welling, 2013) that optimize a lower bound on the marginal data likelihood, and can be extended to learn latent structures (Miao and Blunsom, 2016; Yin et al., 2018). Against the flow-based models, VAEs remove the invertibility constraint but sacrifice the merits of exact inference and exact log likelihood computation, which potentially results in optimization challenges (Kingma et al., 2016).
Answer: It sacrifices the merits of exact inference and exact log likelihood computation.
Question: How much does the new approach outperform the baseline?
Evidence: We study textual autocomplete---the task of predicting a full sentence from a partial sentence---as a human-machine communication game. Specifically, we consider three competing goals for effective communication: use as few tokens as possible (efficiency), transmit sentences faithfully (accuracy), and be learnable to humans (interpretability). We propose an unsupervised approach which tackles all three desiderata by constraining the communication scheme to keywords extracted from a source sentence for interpretability and optimizing the efficiency-accuracy tradeoff. Our experiments show that this approach results in an autocomplete system that is 52% more accurate at a given efficiency level compared to baselines, is robust to user variations, and saves time by nearly 50% compared to typing full sentences.
Our experiments show that this approach results in an autocomplete system that is 52% more accurate at a given efficiency level compared to baselines, is robust to user variations, and saves time by nearly 50% compared to typing full sentences.
Answer: It is 52% more accurate at a given efficiency level compared to baselines.
Question: How many boilers and engines did the Indiana have, put the answers in comma separated ascending order
Evidence: Indiana was 351 feet 2 inches (107.04 m) long overall and had a beam of 69 ft 3 in (21.11 m) and a draft of 24 ft (7.3 m). She displaced 10,288 long tons (10,453 t) as designed and up to 11,688 long tons (11,876 t) at full load. The ship was powered by two-shaft triple-expansion steam engines rated at 9,000 indicated horsepower (6,700 kW) and four coal-fired fire-tube boilers, generating a top speed of 15 knots (28 km/h; 17 mph). She had a cruising radius of 5,640 nautical miles (10,450 km; 6,490 mi) at a speed of 10 knots (19 km/h; 12 mph). As built, she was fitted with a heavy military mast, which was later supplemented by a stern cage mast in 1910–1911. She had a crew of 32 officers and 441 enlisted men, which increased to a total of 586–636 officers and enlisted.
Answer: Two-shaft triple-expansion steam engines, Four coal-fired fire-tube boilers
Question: Give me a list of the five famous things to see in Nepal
Answer: 1. Pashupatinath Temple
2. Boudhanath Stupa
3. Swayambhunath Temple
4. Kathmandu Durbar Square
5. Sarangkot
Question: How to evaluate the effect of shallow syntax in their experiments?
Evidence: Our experiments evaluate the effect of shallow syntax, via contextualization (mSynC, §SECREF2) and features (§SECREF3). We provide comparisons with four baselines—ELMo-transformer BIBREF0, our reimplementation of the same, as well as two cwr-free baselines, with and without shallow syntactic features. Both ELMo-transformer and mSynC are trained on the 1B word benchmark corpus BIBREF19; the latter also employs chunk annotations (§SECREF2). Experimental settings are detailed in Appendix §SECREF22.
Our experiments evaluate the effect of shallow syntax, via contextualization (mSynC, §2) and features (§3).
Answer: Via contextualization (mSynC, §2) and features (§3).
Question: Is there a way to prevent type 1 diabetes?
Evidence: There is no known way to prevent type 1 diabetes. Treatment with insulin is required for survival. Insulin therapy is usually given by injection just under the skin but can also be delivered by an insulin pump. A diabetic diet and exercise are important parts of management. If left untreated, diabetes can cause many complications. Complications of relatively rapid onset include diabetic ketoacidosis and nonketotic hyperosmolar coma. Long-term complications include heart disease, stroke, kidney failure, foot ulcers and damage to the eyes. Furthermore, since insulin lowers blood sugar levels, complications may arise from low blood sugar if more insulin is taken than necessary.
Answer: There is currently no way to prevent type 1 diabetes. Treatment by injection or by an insulin pump is required for survival.
Question: How many additional task-specific layers are introduced?
Evidence: FLOAT SELECTED: Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.
FLOAT SELECTED: Table 1: Optimal hyperparameters used for final training on the ADE and CoNLL04 datasets.
Answer: 2 for the ADE dataset and 3 for the CoNLL04 dataset
Question: Given this paragraph about surfboards, summarize what a surfboard is made of
Evidence: Most modern surfboards are made of fiberglass foam (PU), with one or more wooden strips or "stringers", fiberglass cloth, and polyester resin (PE). An emerging board material is epoxy resin and Expanded Polystyrene foam (EPS) which is stronger and lighter than traditional PU/PE construction. Even newer designs incorporate materials such as carbon fiber and variable-flex composites in conjunction with fiberglass and epoxy or polyester resins. Since epoxy/EPS surfboards are generally lighter, they will float better than a traditional PU/PE board of similar size, shape and thickness. This makes them easier to paddle and faster in the water. However, a common complaint of EPS boards is that they do not provide as much feedback as a traditional PU/PE board. For this reason, many advanced surfers prefer that their surfboards be made from traditional materials.
Answer: Surfboards can be made out of a variety of materials, but most surfboards these days are made from a foam core that is coated in a hard exterior
Question: How long is a mile?
Answer: One mile is 5,280 feet.