DataHammer/scimrc huggingface dataset:
Question: What pre-trained models did they compare to?
Evidence: To test the applicability of the proposed MWA attention, we choose three publicly available Chinese pre-trained models as the basic encoder: BERT, ERNIE, and BERT-wwm. In order to make a fair comparison, we keep the same hyper-parameters (such maximum length, warm-up steps, initial learning rate, etc) as suggested in BERT-wwm BIBREF13 for both baselines and our method on each dataset. We run the same experiment for five times and report the average score to ensure the reliability of results. For detailed hyper-parameter settings, please see Appendix. Besides, three popular CWS tools thulac BIBREF14, ictclas BIBREF15 and hanlp BIBREF16 are employed to segment the Chinese sentences into words.
To test the applicability of the proposed MWA attention, we choose three publicly available Chinese pre-trained models as the basic encoder: BERT, ERNIE, and BERT-wwm. In order to make a fair comparison, we keep the same hyper-parameters (such maximum length, warm-up steps, initial learning rate, etc) as suggested in BERT-wwm BIBREF13 for both baselines and our method on each dataset.
Answer: The answers are shown as follows:
* BERT, ERNIE, and BERT-wwm
Question: Identify which instrument is string or percussion: Damaru, Laouto
Answer: Damaru is percussion, Laouto is string.
Question: Where did David Houseman go to college?
Evidence: Housman received his BA in 1966 and MA in 1971 from Brandeis University. As one of the first postdocs in the lab of Harvey Lodish at MIT, Housman showed that all mammalian proteins begin with a methionine residue transferred from a specific met-initiator tRNA. Between 1973 and 1975 he taught at the University of Toronto and was on the staff of the Ontario Cancer Institute. He joined the MIT faculty in 1975. In his lab at MIT, he mentored Jim Gusella and Daniel Haber.
Answer: David Houseman received his BA and MA in 1966 from Brandeis University.
Question: What's the difference between the conventional stacked LSTMs and the suggested architecture?
Evidence: We propose a method of stacking multiple long short-term memory (LSTM) layers for modeling sentences. In contrast to the conventional stacked LSTMs where only hidden states are fed as input to the next layer, our architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs. Thus the proposed stacked LSTM architecture modulates the amount of information to be delivered not only in horizontal recurrence but also in vertical connections, from which useful features extracted from lower layers are effectively conveyed to upper layers. We dub this architecture Cell-aware Stacked LSTM (CAS-LSTM) and show from experiments that our models achieve state-of-the-art results on benchmark datasets for natural language inference, paraphrase detection, and sentiment classification.
In contrast to the conventional stacked LSTMs where only hidden states are fed as in put to the next layer, the suggested architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lower context using the soft gating mechanism of LSTMs.
Answer: The conventional stacked LSTMs only hidden states are fed as in put to the next layer, and the suggested architecture accepts both hidden and memory cell states of the preceding layer and fuses information from the left and the lotheyr context using the soft gating mechanism of LSTMs.
Question: What is the focus of the paper? Is it the domain adaptation approach or the specific problem of domain adaptation (DA) of autoencoders using an MDN as the channel model?
Evidence: The problem of end-to-end learning of a communication system using an autoencoder has recently been shown to be a promising approach. We focus on the problem of test-time domain adaptation for such an autoencoder system whose channel is generatively-modeled using a mixture density network (MDN). Different from the setting of conventional training-time (unsupervised or semi-supervised) domain adaptation, here we have a fully-trained channel model and autoencoder from a source domain, that we would like to adapt to a target domain using only a small labeled dataset (and no unlabeled data). Moreover, since the distribution of the channel is expected to change frequently (e.g., a wireless link), the error rate of the autoencoder can degrade quickly, making it challenging to collect sufficient data for frequent retraining of the autoencoder. To address this, we propose a fast and sample-efficient method for adapting the autoencoder without modifying the encoder and decoder neural networks, and adapting only the MDN channel model. The method utilizes feature transformations at the decoder to compensate for changes in the channel distribution, and effectively present to the decoder samples close to the source distribution. Experimental evaluation on simulated datasets and real mmWave wireless channels demonstrate that the proposed method can adapt the MDN channel using very limited number of samples, and improve or maintain the error rate of the autoencoder under changing channel conditions.
The problem of end-to-end learning of a communication system using an autoencoder has recently been shown to be a promising approach. We focus on the problem of test-time domain adaptation for such an autoencoder system whose channel is generatively-modeled using a mixture density network (MDN). Different from the setting of conventional training-time (unsupervised or semi-supervised) domain adaptation, here we have a fully-trained channel model and autoencoder from a source domain, that we would like to adapt to a target domain using only a small labeled dataset (and no unlabeled data).
Answer: The main focus of our paper is on the domain adaptation of an end-to-end autoencoder-based communication system, under a frequently-changing channel distribution. We also focus on a generative channel model based on mixture density networks. We agree with the reviewer that our earlier abstract seemed to focus more on the general domain adaptation problem. We have now revised the abstract to focus more on the specific problem of autoencoder and channel adaptation that we address in the paper.
Question: Identify which instrument is string or percussion: Samphor, Viola toeria
Answer: Viola toeria is string, Samphor is percussion.
Question: How big is dataset used to train Word2Vec for the Italian Language?
Evidence: The dataset needed to train the W2V was obtained using the information extracted from a dump of the Italian Wikipedia (dated 2019.04.01), from the main categories of Italian Google News (WORLD, NATION, BUSINESS, TECHNOLOGY, ENTERTAINMENT, SPORTS, SCIENCE, HEALTH) and from some anonymized chats between users and a customer care chatbot (Laila). The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
The dataset (composed of 2.6 GB of raw text) includes $421\,829\,960$ words divided into $17\,305\,401$ sentences.
Answer: The answers are shown as follows:
* $421\,829\,960$ words divided into $17\,305\,401$ sentences
Question: What is Grace Hopper known for?
Answer: Grace Hopper was the first person to create a compiler for a programming language and one of the first programmers of the Harvard Mark I computer, an electro-mechanical computer based on Analytical Engine. Hopper's work with computers started in 1943, when she started working at the Bureau of Ordnance's Computation Project at Harvard where she programmed the Harvard Mark I. Hopper not only programmed the computer, but created a 500-page comprehensive manual for it. Even though Hopper created the manual, which was widely cited and published, she was not specifically credited in it. Hopper is often credited with the coining of the term "bug" and "debugging" when a moth caused the Mark II to malfunction. While a moth was found and the process of removing it called "debugging," the terms were already part of the language of programmers.
Question: What data is used to build the embeddings?
Evidence: pretrained word embeddings (WE): For the PDT experiments, we generate the word embeddings with word2vec on a concatenation of large raw Czech corpora available from the LINDAT/CLARIN repository. For UD Czech, we use FastText word embeddings BIBREF27 of dimension 300, which we pretrain on Czech Wikipedia using segmentation and tokenization trained from the UD data.
BERT BIBREF1: Pretrained contextual word embeddings of dimension 768 from the Base model. We average the last four layers of the BERT model to produce the embeddings. Because BERT utilizes word pieces, we decompose UD words into appropriate subwords and then average the generated embeddings over subwords belonging to the same word.
Flair BIBREF2: Pretrained contextual word embeddings of dimension 4096.
pretrained word embeddings (WE): For the PDT experiments, we generate the word embeddings with word2vec on a concatenation of large raw Czech corpora available from the LINDAT/CLARIN repository. For UD Czech, we use FastText word embeddings BIBREF27 of dimension 300, which we pretrain on Czech Wikipedia using segmentation and tokenization trained from the UD data.

BERT BIBREF1: Pretrained contextual word embeddings of dimension 768 from the Base model. We average the last four layers of the BERT model to produce the embeddings. Because BERT utilizes word pieces, we decompose UD words into appropriate subwords and then average the generated embeddings over subwords belonging to the same word.

Flair BIBREF2: Pretrained contextual word embeddings of dimension 4096.
Answer: The answers are shown as follows:
* large raw Czech corpora available from the LINDAT/CLARIN repository
* Czech Wikipedia
Question: What is the mini-batch size?
Evidence: We apply our adaptively sparse Transformers on four machine translation tasks. For comparison, a natural baseline is the standard Transformer architecture using the softmax transform in its multi-head attention mechanisms. We consider two other model variants in our experiments that make use of different normalizing transformations:
1.5-entmax: a Transformer with sparse entmax attention with fixed $\alpha =1.5$ for all heads. This is a novel model, since 1.5-entmax had only been proposed for RNN-based NMT models BIBREF14, but never in Transformers, where attention modules are not just one single component of the seq2seq model but rather an integral part of all of the model components.
$\alpha $-entmax: an adaptive Transformer with sparse entmax attention with a different, learned $\alpha _{i,j}^t$ for each head.
The adaptive model has an additional scalar parameter per attention head per layer for each of the three attention mechanisms (encoder self-attention, context attention, and decoder self-attention), i.e.,
and we set $\alpha _{i,j}^t = 1 + \operatornamewithlimits{\mathsf {sigmoid}}(a_{i,j}^t) \in ]1, 2[$. All or some of the $\alpha $ values can be tied if desired, but we keep them independent for analysis purposes.
We use a mini-batch size of 8192 tokens and warm up the learning rate linearly until 20k steps, after which it decays according to an inverse square root schedule.
Answer: 8192 tokens.
Question: What are pajamas?
Answer: Pajamas are comfortable, loose-fitting clothing that people wear to bed at night. They are also referred to as “PJs.”
Question: What do authors frame source domain and target domain as respectively?
Evidence: In SQA, each sample is a triple, INLINEFORM0 , where INLINEFORM1 is a question in either spoken or text form, INLINEFORM2 is a multi-sentence spoken-form document, and INLINEFORM3 is the answer in text from. The task of this work is extractive SQA; that means INLINEFORM4 is a word span from the reference transcription of INLINEFORM5 . An overview framework of SQA is shown in Figure FIGREF1 . In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses. Hence, we can collect source domain data more easily, and adapt the model to the target domain.
In this task, when the machine is given a spoken document, it needs to find the answer of a question from the spoken document. SQA can be solved by the concatenation of an ASR module and a question answering module. Given the ASR hypotheses of a spoken document and a question, the question answering module can output a text answer.
The most intuitive way to evaluate the text answer is to directly compute the Exact Match (EM) and Macro-averaged F1 scores (F1) between the predicted text answer and the ground-truth text answer. We used the standard evaluation script from SQuAD BIBREF1 to evaluate the performance.
In this paper, we frame the source domain as reference transcriptions and the target domain as ASR hypotheses.  Hence, we can collect source domain data more easily, and adapt the model to the target domain.
Answer: Reference transcriptions and ASR hypotheses.
Question: How do you make an electronic music song?
Answer: There are quite a few genres in electronic music in which electronic music songs are classified into. A couple of the most popular ones are techno, house, progressive house, trance, breakbeats, drum and bass, dubstep, and many more. With all of these styles, making an electronic music songs is the same. You need to make a beat using a combination of drums, kicks, snares, claps, high hats, toms, and samples. These are organized in a way to make a beat and loop through these rhythms. These rhythms are then layered in a way to help create the foundation for a song. Many popular tools that are used are digital audio workstations also known as a DAW. Drum machines, samplers, and more. Besides the beats and samples, many times other instruments are used such as synthesizers and vocoders. All of this is arranged using the DAW tool to create a song.
Question: On a cross country roadtrip across the United States where we expect to drive for many hours per day, categorize each of the following as either 'necessary', 'somewhat necessary' or 'nice to have'. A bathroom break, a music playlist, snacks, a map.
Answer: On a cross country roadtrip across the United States a bathroom break is necessary.
On a cross country roadtrip across the United States a music playlist is nice to have.
On a cross country roadtrip across the United States snacks are nice to have.
On a cross country roadtrip across the United States a a map is somewhat necessary.
Question: Identify which instrument is string or percussion: Malimba, Langspil
Answer: Langspil is string, Malimba is percussion.
Question: How can one loose weight
Answer: There are several ways to shed some pounds which include having a balanced diet which is low in fats and carbohydrates, regular workouts or sports activities like running, brisk walking, swimming and hitting the gym etc. Yoga is also a good alternative to attain a healthy lifestyle and keep your weight in check. Some people use mobile applications to record their daily calorie intake and try to eat little less than the daily recommended calories.
Question: What do the authors plan to do in their future work?
Evidence: In this paper, we proposed a method of stacking multiple LSTM layers for modeling sentences, dubbed CAS-LSTM. It uses not only hidden states but also cell states from the previous layer, for the purpose of controlling the vertical information flow in a more elaborate way. We evaluated the proposed method on various benchmark tasks: natural language inference, paraphrase identification, and sentiment classification. Our models achieve the new state-of-the-art accuracy on SNLI and Quora Question Pairs datasets and obtain comparable results on MultiNLI and SST datasets. The proposed architecture can replace any stacked LSTM under one weak restriction—the size of states should be identical across all layers.
For future work we plan to apply the CAS-LSTM architecture beyond sentence modeling tasks. Various problems e.g. sequence labeling, sequence generation, and language modeling might benefit from sophisticated modulation on context integration. Aggregating diverse contexts from sequential data, e.g. those from forward and backward reading of text, could also be an intriguing research direction.
For future work we plan to apply the CAS-LSTM architecture beyond sentence modeling tasks. Various problems such as sequence labeling and language modeling might benefit from sophisti cated modulation on context integration. Aggregating diverse contexts from sequential data, e.g. those from forward and backward reading of text, could also be an intriguing research direction.
Answer: For future work they plan to apply the CAS-LSTM architecture beyond sentence modeling tasks.
Question: How strong was the correlation between exercise and diabetes?
Evidence: The main DDEO topics showed some level of interrelationship by appearing as subtopics of other DDEO topics. The words with italic and underline styles in Table 2 demonstrate the relation among the four DDEO areas. Our results show users' interest about posting their opinions, sharing information, and conversing about exercise & diabetes, exercise & diet, diabetes & diet, diabetes & obesity, and diet & obesity (Figure FIGREF9 ). The strongest correlation among the topics was determined to be between exercise and obesity ( INLINEFORM0 ). Other notable correlations were: diabetes and obesity ( INLINEFORM1 ), and diet and obesity ( INLINEFORM2 ).
FLOAT SELECTED: Figure 2: DDEO Correlation P-Value
The main DDEO topics showed some level of interrelationship by appearing as subtopics of other DDEO topics.
Our results show users' interest about posting their opinions, sharing information, and conversing about exercise & diabetes, exercise & diet, diabetes & diet, diabetes & obesity, and diet & obesity (Figure FIGREF9 ).
FLOAT SELECTED: Figure 2: DDEO Correlation P-Value
Answer: weak correlation with p-value of 0.08
Question: What is the baseline of this paper?
Evidence: Recently, with the emergence of neural seq2seq models, abstractive summarization methods have seen great performance strides BIBREF0, BIBREF1, BIBREF2. However, complex neural summarization models with thousands of parameters usually require a large amount of training data. In fact, much of the neural summarization work has been trained and tested in news domains where numerous large datasets exist. For example, the CNN/DailyMail (CNN/DM) BIBREF3, BIBREF4 and New York Times (NYT) datasets are in the magnitude of 300k and 700k documents, respectively. In contrast, in other domains such as student reflections, summarization datasets are only in the magnitude of tens or hundreds of documents (e.g., BIBREF5). We hypothesize that training complex neural abstractive summarization models in such domains will not yield good performing models, and we will indeed later show that this is the case for student reflections.
To improve performance in low resource domains, we explore three directions. First, we explore domain transfer for abstractive summarization. While domain transfer is not new, compared to prior summarization studies BIBREF6, BIBREF7, our training (news) and tuning (student reflection) domains are quite dissimilar, and the in-domain data is small. Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline. Lastly, we combine both directions. Evaluations of neural abstractive summarization method across four student reflection corpora show the utility of all three methods.
Second, we propose a template-based synthesis method to create synthesized summaries, then explore the effect of enriching training data for abstractive summarization using the proposed model compared to a synthesis baseline.
Answer: A synthesis baseline.
Question: List the founders of Twitter from the above passage in a comma separated format.
Evidence: Twitter is an online social media and social networking service owned and operated by American company Twitter, Inc., on which users post or reply to texts, images and videos known as "tweets". Registered users can tweet, like, "retweet" tweets and direct message (DM), while unregistered users only have the ability to view public tweets. Users interact with Twitter through browser or mobile frontend software, or programmatically via its APIs.

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.

On October 27, 2022, business magnate Elon Musk acquired Twitter, Inc. for US$44 billion, gaining control of the platform. On December 20, 2022, Musk announced he would step down as CEO once a replacement had been found. As of April 2023, Musk has yet to announce a successor and remains CEO.
Answer: Jack Dorsey, Noah Glass, Biz Stone, Evan Williams
Question: What's a good time of year to visit Seattle?
Answer: The best time of year to visit Seattle is in the summer. The weather is nicest from June to September, and there are many outdoor activities to enjoy. However, there is lots to do all year. For example, in spring many visitors come to the University of Washington campus to see cherry blossoms. The nearby tulip festival in the Skagit Valley is another popular spring attraction.
Question: Extract the list of science fiction awards from the paragraph below in the form of bulleted list, each item should include award name and whom is it presented by.
Evidence: Among the most respected and well-known awards for science fiction are the Hugo Award for literature, presented by the World Science Fiction Society at Worldcon, and voted on by fans; the Nebula Award for literature, presented by the Science Fiction and Fantasy Writers of America, and voted on by the community of authors; the John W. Campbell Memorial Award for Best Science Fiction Novel, presented by a jury of writers; and the Theodore Sturgeon Memorial Award for short fiction, presented by a jury. One notable award for science fiction films and TV programs is the Saturn Award, which is presented annually by The Academy of Science Fiction, Fantasy, and Horror Films.
Answer: - Hugo Award for literature, presented by the World Science Fiction Society.
- Nebula Award for literature, presented by the Science Fiction and Fantasy Writers of America.
- John W. Campbell Memorial Award for Best Science Fiction Novel, presented by a jury of writers.
- Theodore Sturgeon Memorial Award for short fiction, presented by a jury.
- Saturn Award, presented by by The Academy of Science Fiction, Fantasy, and Horror Films.