DataHammer/scimrc huggingface dataset:
Question: Which two models did the author develop?
Evidence: Emotion detection has long been a topic of interest to scholars in natural language processing (NLP) domain. Researchers aim to recognize the emotion behind the text and distribute similar ones into the same group. Establishing an emotion classifier can not only understand each user's feeling but also be extended to various application, for example, the motivation behind a user's interests BIBREF0. Based on releasing of large text corpus on social media and the emotion categories proposed by BIBREF1, BIBREF2, numerous models have provided and achieved fabulous precision so far. For example, DeepMoji BIBREF3 which utilized transfer learning concept to enhance emotions and sarcasm understanding behind the target sentence. CARER BIBREF4 learned contextualized affect representations to make itself more sensitive to rare words and the scenario behind the texts.
As methods become mature, text-based emotion detecting applications can be extended from a single utterance to a dialogue contributed by a series of utterances. Table TABREF2 illustrates the difference between single utterance and dialogue emotion recognition. The same utterances in Table TABREF2, even the same person said the same sentence, the emotion it convey may be various, which may depend on different background of the conversation, tone of speaking or personality. Therefore, for emotion detection, the information from preceding utterances in a conversation is relatively critical.
In SocialNLP 2019 EmotionX, the challenge is to recognize emotions for all utterances in EmotionLines dataset, a dataset consists of dialogues. According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT) BIBREF5, FriendsBERT and ChatBERT. In this paper, we introduce our approaches including causal utterance modeling, model pre-training, and fine-turning.
According to the needs for considering context at the same time, we develop two classification models, inspired by bidirectional encoder representations from transformers (BERT, FriendsBERT and ChatBERT.
Answer: FriendsBERT and ChatBERT.
Question: What are some items you can put on a bookshelf, other than books, to make it nice looking.
Answer: Peppering a bookshelf with items other than books can be aesthetically pleasing. Some ideas for items to add to a bookshelf include small containers or boxes to hold small items, pictures in picture frames, book ends, trinkets, polished stones or crystals, candles, statues, plants, flower arrangements (living or dried), a standing clock, antique machines, a lamp, or a globe.
Question: what are the existing models they compared with?
Evidence: Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models:
BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;
QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;
BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;
and numerical MRC models:
NAQANet BIBREF6, a numerical version of QANet model.
NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. “2.5”), richer arithmetic expression, data augmentation, etc. The enhancements are also used in our NumNet model and the details are given in the Appendix.
Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;
OpenIE BIBREF6, KDG with open information extraction based sentence representations;
SRL BIBREF6, KDG with semantic role labeling based sentence representations;
and traditional MRC models:
Experiments ::: Baselines
For comparison, we select several public models as baselines including semantic parsing models:
BiDAF BIBREF3, an MRC model which utilizes a bi-directional attention flow network to encode the question and passage;

QANet BIBREF12, which utilizes convolutions and self-attentions as the building blocks of encoders to represent the question and passage;

BERT BIBREF23, a pre-trained bidirectional Transformer-based language model which achieves state-of-the-art performance on lots of public MRC datasets recently;

and numerical MRC models:
NAQANet BIBREF6, a numerical version of QANet model.

NAQANet+, an enhanced version of NAQANet implemented by ourselves, which further considers real number (e.g. “2.5”), richer arithmetic expression, data augmentation, etc.
Syn Dep BIBREF6, the neural semantic parsing model (KDG) BIBREF22 with Stanford dependencies based sentence representations;

OpenIE BIBREF6, KDG with open information extraction based sentence representations;

SRL BIBREF6, KDG with semantic role labeling based sentence representations;

and traditional MRC models:
Answer: The answers are shown as follows:
* Syn Dep
* OpenIE
* SRL
* BiDAF
* QANet
* BERT
* NAQANet
* NAQANet+
Question: Write an email to someone updating that I’ve accepted the position at their company to which they referred me
Answer: Hi, 

I wanted to update you that I accepted the offer!

Thank you for being such a huge part of this, from coffee chats to interview prepping. You made all the difference in my application journey and I’m so excited to work with you.
Question: List the teams that have won IPL.
Evidence: The Indian Premier League (IPL) (also known as the TATA IPL for sponsorship reasons) is a men's Twenty20 (T20) cricket league held annually in India and contested by ten city-based franchise teams. The league was founded by the Board of Control for Cricket in India (BCCI) in 2007. Brijesh Patel is the incumbent chairman of the IPL. The competition is usually held annually in summer (between March and May) and has an exclusive window in the ICC Future Tours Programme, meaning that less international cricket take place during the IPL seasons.
The IPL is the most popular cricket league in the world, and in 2014 it was ranked sixth by average attendance among all sports leagues.[needs update] In 2010, the IPL became the first sporting event in the world to be broadcast live on YouTube. The brand value of the IPL in 2022 was ₹90,038 crore (US$11 billion). According to BCCI, the 2015 IPL season contributed ₹1,150 crore (US$140 million) to the GDP of the Indian economy. In December 2022, the league became a decacorn valued at $10.9 billion registering a 75% growth in dollar terms since 2020 when it was valued at $6.2 billion, according to a report by consulting firm D & P Advisory.
With five titles, Mumbai Indians are the most successful team in the league's history in terms of the number of titles won. The Chennai Super Kings have won four titles, the Kolkata Knight Riders have won two, and four other teams, Rajasthan Royals, Deccan Chargers, Sunrisers Hyderabad and Gujarat Titans have won the title once.
Answer: The teams that have won IPL are Mumbai Indians, Chennai Super Kings, Kolkata Knight Riders, Rajasthan Royals, Deccan Chargers, Sunrisers Hyderabad and Gujarat Titans.
Question: Why do people enjoy surfing
Answer: Surfing is enjoyable because it connects you with the ocean and can often be seen as therapeutic. Additionally surfing can often be a great form of exercise which helps maintain a healthy lifestyle. Lastly, surfing is done in the ocean and typically near a beach. People enjoy the atmosphere and joy the beach brings.
Question: What is "the Scully effect?"
Answer: The Scully Effect, named for the science-minded character Dana Scully from the sci-fi TV show "The X-Files," is the term used to suggest the impact that the character had on influencing women to pursue careers in science, technology, engineering, and math.
Question: What does Pinot Noir mean?
Evidence: Pinot noir (French: [pino nwaʁ]) is a red-wine grape variety of the species Vitis vinifera. The name may also refer to wines created predominantly from pinot noir grapes. The name is derived from the French words for pine and black. The word pine alludes to the grape variety having tightly clustered, pinecone–shaped bunches of fruit.
Answer: The name Pinot Noir is derived from the French words for pine and black.
Question: Find the bird in this list: Shark, Dog, Arctic tern
Answer: Arctic tern
Question: What method has the author adapted?
Evidence: Early research efforts, such as the TDT program BIBREF5 , have studied news clustering for some time. The problem of online monolingual clustering algorithms (for English) has also received a fair amount of attention in the literature. One of the earlier papers by aggarwal2006framework introduced a two-step clustering system with both offline and online components, where the online model is based on a streaming implementation of INLINEFORM0 -means and a bag-of-words document representation. Other authors have experimented with distributed representations, such as ahmed2011unified, who cluster news into storylines using Markov chain Monte Carlo methods, rehureklrec who used incremental Singular Value Decomposition (SVD) to find relevant topics from streaming data, and sato2017distributed who used the paragraph vector model BIBREF6 in an offline clustering setting.
More recently, crosslingual linking of clusters has been discussed by rupnik2016news in the context of linking existing clusters from the Event Registry BIBREF7 in a batch fashion, and by steinberger2016mediagist who also present a batch clustering linking system. However, these are not “truly” online crosslingual clustering systems since they only decide on the linking of already-built monolingual clusters. In particular, rupnik2016news compute distances of document pairs across clusters using nearest neighbors, which might not scale well in an online setting. As detailed before, we adapted the cluster-linking dataset from rupnik2016news to evaluate our online crosslingual clustering approach. Preliminary work makes use of deep learning techniques BIBREF8 , BIBREF9 to cluster documents while learning their representations, but not in an online or multilingual fashion, and with a very small number of cluster labels (4, in the case of the text benchmark).
In our work, we studied the problem of monolingual and crosslingual clustering, having experimented several directions and methods and the impact they have on the final clustering quality. We described the first system which aggregates news articles into fine-grained story clusters across different languages in a completely online and scalable fashion from a continuous stream.
As detailed before, we adapted the cluster-linking dataset from Rupnik et al. to evaluate our online crosslingual clustering approach.
Answer: They adapted the cluster-linking dataset from Rupnik et al. to evaluate their online crosslingual clustering approach.
Question: How many documents were used for training in the 20 newsgroups dataset?
Evidence: 20 newsgroups data set is one of the frequently used datasets in the text processing community for text classification and text clustering. This data set contains approximately 20,000 English documents from 20 topics to be identified, with 11314 documents for training and 7532 for testing. In this work, we used only 90% of documents for training and the remaining 10% for validation. For fair comparison with other publications, we used 53160 words vocabulary set available in the datasets website.
20 newsgroups3 data set is one of the frequently used datasets in the text processing community for text classification and text clustering. This data set contains approximately 20,000 English documents from 20 topics to be identified, with 11314 documents for training and 7532 for testing.
Answer: 11314 documents.
Question: What was the compelling event that shut down the IRRI station?
Evidence: IRRI station is a railway station located on the South Main Line in Los Baños, Laguna, Philippines. It is a flag stop for the line as there are no platforms yet being erected, temporary stairs for the trains are added in the meantime to facilitate loading and unloading.

History
In December 2019, the flag stop was opened as PNR extended the Metro South Commuter trips by adding 5 more stations on the present commuter line. KiHa 59 series and KiHa 35 trainsets ply the route, with the former servicing the entire route to Tutuban and the latter going up to Alabang only. The station served as the southern terminus of the newly opened line.

Services was disrupted as soon as the lockdown caused by the COVID-19 Pandemic takes effect mid-March 2020. As of October 2021, the service is still inactive.

A passing loop was planned for possible use of locomotives in the station but only the switch was laid. This plan was not realized as of October 2021.

In January 2022, the railway switch and the steel stairs was dismantled by PNR Crew along with DEL 5007 to be repurposed for the upcoming Inter-Provincial Commuter Train Service between San Pablo City in the province of Laguna and Lucena City in the province of Quezon. Only some dismantled rail pieces and railfrogs remain scattered in the area of the flagstop. In May 25, 2022, an inspection train hailing from Dela Rosa Station travelled to IRRI Flagstop with officials onboard to conduct certification of the railway from Manila to Los Banos for possible reopening of commuter services along with the San Pablo-Lucena Commuter Line. The trainset used consist of DHL-9003, PC 8303, with DEL 5007 at the end serving as a back engine. As of July 2022 only the line connecting Laguna and Quezon Province had been realised while the Dela Rosa-IRRI-San Pablo is still pending due to lack of available train.
Answer: Unfortunately, the IRRI station railway located on the South Main LIne in Los Banos, Laguna, Philippines became inactive in mid-March of 2020 due to COVID lockdowns.
Question: Which states are part of the New England region?
Answer: The states in New England include Massachusetts, New Hampshire, Maine, Vermont, Rhode Island, and Connecticut.
Question: What is the most powerful Porsche flat six engine?
Evidence: The Porsche flat-six engine series is a line of mechanically similar, naturally aspirated and turbocharged, flat-six boxer engines, produced by Porsche for almost 60 consecutive years, since 1963. The engine is an evolution of the flat-four boxer used in the original Volkswagen Beetle.


1967 Porsche 911R
0:22
2006 Porsche Cayman S
0:34
Problems playing these files? See media help.
The flat-six engine is most often associated with their 911 model, Porsche's flagship rear-engined sports car which has used flat-six engines exclusively since 1963. The engines were air-cooled until 1999, when Porsche started using water-cooled engines.

In April 2011, Porsche announced the third generation of the 997 GT3 RS with an enlarged 4.0-litre engine having a power output of 500 PS (368 kW; 493 hp). The naturally-aspirated 4.0-litre flat-six engine (the largest engine offered in a street-legal 911) was introduced with their 911 (997) GT3 RS 4.0, in 2011. The engine itself uses the crankshaft from the RSR with increased stroke dimensions (from 76.4 mm to 80.4 mm). This change increased the power output to 500 PS (368 kW; 493 hp) at 8,250 rpm and 460 N⋅m (339 lbf⋅ft) of torque at 5,750 rpm. giving it a power-to-weight ratio of 365 hp per ton. Only 600 cars were built. At 493 hp (368 kW), the engine is one of the most powerful six-cylinder naturally aspirated engines in any production car with a 123.25 hp (92 kW) per litre output.

Other Porsche models that use flat-six engines are the 1970–1972 Porsche 914/6 (mid-engine), the 1986–1993 Porsche 959 (rear-engine), and the 1996–2021 Porsche Boxster/Cayman (mid-engine).

The Porsche 962 sports prototype also used a twin-turbocharged flat-six engine.

These engines have also been used by German tuning company RUF in various replica Porsche sports cars.
Answer: The most powerful flat six engine made by Porsche is the 997 GT3 RS 4.0. It had nearly 493hp on just 4.0L of displacement. This is a very rare car that hardly ever come up for sale. It is one of the most powerful six-cylinder naturally aspirated engines ever made.
Question: How do they determine which words are informative?
Evidence: Data dependent regularization. As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words. Here we conduct an experiment to exam the effect. We used a cutoff of 100 in this experiment. Table TABREF24 lists the words having the smallest INLINEFORM0 norm of embeddings found by different algorithms. The number inside the parenthesis after each word is the number of times this word appears in the learning set. In word2Vec or Paragraph Vectors, the least frequent words have embeddings that are close to zero, despite some of them being indicative of sentiment such as debacle, bliss and shabby. In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.
As explained in Section SECREF15 , the corruption introduced in Doc2VecC acts as a data-dependent regularization that suppresses the embeddings of frequent but uninformative words.
In contrast, Doc2VecC manages to clamp down the representation of words frequently appear in the training set, but are uninformative, such as symbols and stop words.
Answer: Informative are those that will not be suppressed by regularization performed.
Question: Did the CIT model outperform a simple fine-tuned model in terms of ROUGE scores on both datasets?
Evidence: Pre-trained language models such as BERT BIBREF0 have significantly improved the accuracy of various language processing tasks. However, we cannot apply BERT to language generation tasks as is because its model structure is not suitable for language generation. Several pre-trained seq-to-seq models for language generation BIBREF1, BIBREF2 based on an encoder-decoder Transformer model, which is a standard model for language generation, have recently been proposed. These models have achieved blackstate-of-the-art results in various language generation tasks, including abstractive summarization.
However, when generating a summary, it is essential to correctly predict which part of the source text should be included in the summary. Some previous studies without pre-training have examined combining extractive summarization with abstractive summarization BIBREF3, BIBREF4. Although pre-trained seq-to-seq models have achieved higher accuracy compared to previous models, it is not clear whether modeling “Which part of the source text is important?” can be learned through pre-training.
blackThe purpose of this study is to clarify the blackeffectiveness of combining saliency models that identify the important part of the source text with a pre-trained seq-to-seq model in the abstractive summarization task. Our main contributions are as follows:
We investigated nine combinations of pre-trained seq-to-seq and token-level saliency models, where the saliency models share the parameters with the encoder of the seq-to-seq model or extract important tokens independently of the encoder.
We proposed a new combination model, the conditional summarization model with important tokens (CIT), in which a token sequence extracted by a saliency model is explicitly given to a seq-to-seq model as an additional input text.
We evaluated the combination models on the CNN/DM BIBREF5 and XSum BIBREF6 datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets.
We evaluated the combination models on the CNN/DM and XSum datasets. Our CIT model outperformed a simple fine-tuned model in terms of ROUGE scores on both datasets.
Answer: Yes, it did.
Question: Tell me which Pokemon are flying or not flying: Pidgeot, Fearow, Charizard, Ditto, Eevee, Chansey
Answer: Pidgeot: Flying
Fearow: Flying
Charizard: Flying
Ditto: Not Flying
Eevee: Not Flying
Chansey: Not Flying
Question: What kind of attention do the authors' models use?
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.
Our models use attention similar to to get object level image representations from Faster R-CNN model.
Answer: Get object level image representations from Faster R-CNN model
Question: What is the usage of the QACNN Layer?
Evidence: In this question answering task, a reading passage , a query and several answer choices are given. P denotes the passage, Q denotes query and C denotes one of the multiple choices. The target of the model is to choose a correct answer A from multiple choices based on informations of P and Q.
Fig. FIGREF1 is the pipeline overview of QACNN. First, we use embedding layer to transform P, Q, and C into word embedding. Then the compare layer generates passage-query similarity map INLINEFORM0 and passage-choice similarity map INLINEFORM1 . The following part is the main component of QACNN. It consists of two-staged CNN architecture. The first stage projects word-level feature into sentence-level, and the second stage projects sentence-level feature into passage-level. Moreover, we apply query-based attention mechanism to each stage on the basis of INLINEFORM2 feature at word level and sentence level respectively. After QACNN Layer, we obtain each choice answer feature. Finally, a prediction layer collects output information from every choice feature and returns the most possible answer.
We propose an attention convolutional matching layer to inte grate two similarity maps given above. That is, QACNN Layer is used to learn the location relationship pattern. It contains a two-staged CNN combined with query-based attention mechanism. Each stage comprises two major part: attention map and output representation.
Answer: QACNN Layer is used to learn the location relationship pattern.
Question: From the passage mention the awards received by Sunil Manohar Gavaskar. Display the results in comma separated format.
Evidence: Sunil Manohar Gavaskar (Marathi pronunciation: [suniːl ɡaːʋəskəɾ]; pronunciation (help·info); born 10 July 1949), is an Indian cricket commentator and former cricketer who represented India and Bombay from 1971 to 1987. Gavaskar is acknowledged as one of the greatest opening batsmen of all time.

Gavaskar was widely admired for his technique against fast bowling, with a particularly high average of 65.45 against the West Indies, who possessed a four-pronged fast bowling attack, widely regarded as the most vicious in Test history. However, most of Gavaskar's centuries against West Indies were against their second string team when their four-pronged attack were not playing together  His captaincy of the Indian team, was considered as one of the first attacking ones, with Indian team winning the 1984 Asia Cup, and the Benson & Hedges World Championship of Cricket in 1985. At the same time, there were multiple exchanges of captaincy between Gavaskar and Kapil Dev, with one coming just six months before Kapil led India to victory at the 1983 Cricket World Cup. He is also a former Sheriff of Mumbai.

Gavaskar is a recipient of the Indian sports honour of the Arjuna Award and the civilian honour of the Padma Bhushan. He was inducted into the ICC Cricket Hall of Fame in 2009. In 2012, he was awarded the Col CK Nayudu Lifetime Achievement Award for Cricket in India.
Answer: Arjuna Award, Padma Bhushan, inducted into the ICC Cricket Hall of Fame, Col CK Nayudu Lifetime Achievement Award