DataHammer/scimrc huggingface dataset:
Question: On a backcountry backpacking trip where we expect to cross waterways, categorize each of the following as either 'useful', 'not useful', 'dangerous' or 'neither'.  A chinchilla, a compass, granola, dry bag, seventeen oars, thermite, ten tons of uranium ore
Answer: On a backcountry backpacking trip where we expect to cross waterways, a chinchilla is neither dangerous nor helpful, a compass is very useful, a granola is useful, a dry bag us useful, seven oars are super useful, a thermite is not useful and ten tones of uranium ore are extremely dangerous
Question: Considering harassment types, what are the three subcategories of tweets?
Evidence: The dataset from Twitter that we are using in our work, consists of a train set, a validation set and a test set. It was published for the "First workshop on categorizing different types of online harassment languages in social media". The whole dataset is divided into two categories, which are harassment and non-harassment tweets. Moreover, considering the type of the harassment, the tweets are divided into three sub-categories which are indirect harassment, sexual and physical harassment. We can see in Table TABREF1 the class distribution of our dataset. One important issue here is that the categories of indirect and physical harassment seem to be more rare in the train set than in the validation and test sets. To tackle this issue, as we describe in the next section, we are performing data augmentation techniques. However, the dataset is imbalanced and this has a significant impact in our results.
Moreover, considering the type of the harassment, the tweets are divided into three sub-categories which are indirect harassment, sexual and physical harassment.
Answer: Indirect harassment, sexual and physical harassment.
Question: What baseline models are offered?
Evidence: n-gram: We consider 3-gram and 4-gram conditional language model baseline with interpolation. We use random grid search for the best coefficients for the interpolated model.
Convolution: We use the fconv architecture BIBREF24 and default hyperparameters from the fairseq BIBREF25 framework. We train the network with ADAM optimizer BIBREF26 with learning rate of 0.25 and dropout probability set to 0.2.
LSTM: We consider LSTM models BIBREF27 with and without attention BIBREF28 and use the tensor2tensor BIBREF29 framework for the LSTM baselines. We use a two-layer LSTM network for both the encoder and the decoder with 128 dimensional hidden vectors.
Transformer: As with LSTMs, we use the tensor2tensor framework for the Transformer model. Our Transformer BIBREF21 model uses 256 dimensions for both input embedding and hidden state, 2 layers and 4 attention heads. For both LSTMs and Transformer, we train the model with ADAM optimizer ($\beta _{1} = 0.85$, $\beta _{2} = 0.997$) and dropout probability set to 0.2.
GPT-2: Apart from supervised seq2seq models, we also include results from pre-trained GPT-2 BIBREF30 containing 117M parameters.
n-gram: We consider 3-gram and 4-gram conditional language model baseline with interpolation. We use random grid search for the best coefficients for the interpolated model.

Convolution: We use the fconv architecture BIBREF24 and default hyperparameters from the fairseq BIBREF25 framework. We train the network with ADAM optimizer BIBREF26 with learning rate of 0.25 and dropout probability set to 0.2.

LSTM: We consider LSTM models BIBREF27 with and without attention BIBREF28 and use the tensor2tensor BIBREF29 framework for the LSTM baselines. We use a two-layer LSTM network for both the encoder and the decoder with 128 dimensional hidden vectors.

Transformer: As with LSTMs, we use the tensor2tensor framework for the Transformer model. Our Transformer BIBREF21 model uses 256 dimensions for both input embedding and hidden state, 2 layers and 4 attention heads. For both LSTMs and Transformer, we train the model with ADAM optimizer ($\beta _{1} = 0.85$, $\beta _{2} = 0.997$) and dropout probability set to 0.2.

GPT-2: Apart from supervised seq2seq models, we also include results from pre-trained GPT-2 BIBREF30 containing 117M parameters.
Answer: The answers are shown as follows:
* 3-gram and 4-gram conditional language model
* Convolution
* LSTM models BIBREF27 with and without attention BIBREF28
* Transformer
* GPT-2
Question: How to take care of indoor plants?
Answer: In general, there are three things to consider when taking care of indoor plants. First, plants need to be watered regularly. Second, plants need to be placed in a location where they receive a sufficient amount of light. Finally, plants need to be repotted occasionally. While these three tasks apply to every indoor plant, individual plants have different requirements. For example, some plants need more water while others should only be watered rarely. In terms of light exposure, some plants require a lot of direct light whereas others need to be placed in a less bright location. Hence, it is important to understand the specifics of each plant.
Question: Does Palmeiras have a Fifa club world cup?
Answer: Despite many claims that Palmeiras won a club world cup in 1951, the official list of the Fifa World Club winners starts in the year 2000 with Corinthians being the first champion of the tournament and Palmeiras doesn't have won any Fifa World Cup tournament since.
Question: What datasets are compared?
Evidence: We do comparisons with three sportball datasets, as follows:
Original English (English-sportball): The original MS-COCO English dataset with 3,850 sportball images. This dataset is first evaluated in order to have base results for following comparisons.
Google-translated Vietnamese (GT-sportball): The translated MS-COCO English dataset into Vietnamese using Google Translation API, categorized into sportball.
Manually-annotated Vietnamese (UIT-ViIC): The Vietnamese dataset built with manually written captions for images from MS-COCO, categorized into sportball.
We do comparisons with three sportball datasets, as follows:Original English (English-sportball)– Google-translated Vietnamese (GT-sportball) Manually-annotated Vietnamese (UIT-ViIC).
Answer: English-sportball,UIT-ViIC,GT-sportball are used to compare.
Question: What is a house?
Answer: A house is typically a physical structure for residential living. It has doors to enter and usually different designated for different purpose. It can also have a garage and multiple floors.
Question: What dataset/corpus is this work evaluated over?
Evidence: Word embeddings INLINEFORM0 are initialized with GloVe BIBREF25 . The hidden dimension of each encoder and decoder LSTM cell ( INLINEFORM1 ) is 1024. We use Adam optimizer BIBREF26 and clip the gradients to between -5 and 5. Number of layers, dropout, and non-linearity for image feature prediction layers are 4, 0.3 and ReLU BIBREF27 respectively. Dimensionality of hidden attention layers ( INLINEFORM3 ) is 350 and number of attentions ( INLINEFORM4 ) is 30. We employ orthogonal initialization BIBREF28 for recurrent weights and xavier initialization BIBREF29 for all others. For the datasets, we use Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10 . Image features are prepared by extracting hidden representations at the final layer of ResNet-101 BIBREF30 . We evaluate sentence representation quality using SentEval BIBREF7 , BIBREF1 scripts. Mini-batch size is 128 and negative samples are prepared from remaining data samples in the same mini-batch.
For the datasets, we use Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10 .
Answer: The answers are shown as follows:
* Karpathy and Fei-Fei's split for MS-COCO dataset BIBREF10
Question: Which member of *NSYNC was a replacement for another who quit before they got big?
Answer: Lance Bass.
Question: What approach do they use to train the embeddings?
Evidence: Word embeddings are distributed, real-valued vector representations of discrete words BIBREF25 , BIBREF26 . Compared with one-hot representation, word embeddings are low-dimensional and dense, measuring word meanings in a continuous vector space. Studies show that the offset of two words' embeddings represents a certain relation, e.g., “man” INLINEFORM0 “woman” INLINEFORM1 “king” INLINEFORM2 “queen” BIBREF25 . Hence, it is suitable to use word embeddings to model short and noisy conversation utterances.
To train the embeddings, we adopt the word2vec approach. The idea is to map a word INLINEFORM0 and its context INLINEFORM1 to vectors ( INLINEFORM2 and INLINEFORM3 ). Then we estimate the probability of a word by DISPLAYFORM0 
The goal of word embedding learning is to maximize the average probability of all words (suppose we have INLINEFORM0 running words): DISPLAYFORM0 
We used hierarchical softmax to approximate the probability.
To model the context, we further adopt the continuous bag-of-words (CBOW) method. The context is defined by the sum of neighboring words' (input) vectors in a fixed-size window ( INLINEFORM0 to INLINEFORM1 ) within a sentence: DISPLAYFORM0 
Notice that the context vector INLINEFORM0 in Equation ( EQREF12 ) and the output vector INLINEFORM1 in Equation ( EQREF9 ) are different as suggested in BIBREF25 , BIBREF26 , but the details are beyond the scope of our paper.
Virtual Sentences
In a conversation corpus, successive sentences have a stronger interaction than general texts. For example, in Figure FIGREF2 , the words thank and welcome are strongly correlated, but they hardly appear in the a sentence and thus a same window. Therefore, traditional within-sentence CBOW may not capture the interaction between a query and its corresponding reply.
In this paper, we propose the concept of virtual sentences to learn word embeddings for conversation data. We concatenate a query INLINEFORM0 and its reply INLINEFORM1 as a virtual sentence INLINEFORM2 . We also use all words (other than the current one) in the virtual sentence as context (Figure 2). Formally, the context INLINEFORM3 of the word INLINEFORM4 is given by DISPLAYFORM0 
In this way, related words across two successive utterances from different agents can have interaction during word embedding learning. As will be shown in Subsection SECREF22 , virtual sentences yield a higher performance for dialogue segmentation.
To train the embeddings, we adopt the word2vec approach.
Answer: The word2vec approach.
Question: Tell me which of these countries has more than 1 billion population: Japan, Canada, United States, Russia, China, India
Answer: China and India
Question: From the passage, note down the components of Mortgage.
Evidence: According to Anglo-American property law, a mortgage occurs when an owner (usually of a fee simple interest in realty) pledges his or her interest (right to the property) as security or collateral for a loan. Therefore, a mortgage is an encumbrance (limitation) on the right to the property just as an easement would be, but because most mortgages occur as a condition for new loan money, the word mortgage has become the generic term for a loan secured by such real property. As with other types of loans, mortgages have an interest rate and are scheduled to amortize over a set period of time, typically 30 years. All types of real property can be, and usually are, secured with a mortgage and bear an interest rate that is supposed to reflect the lender's risk.

Mortgage lending is the primary mechanism used in many countries to finance private ownership of residential and commercial property (see commercial mortgages). Although the terminology and precise forms will differ from country to country, the basic components tend to be similar:

Property: the physical residence being financed. The exact form of ownership will vary from country to country and may restrict the types of lending that are possible.
Mortgage: the security interest of the lender in the property, which may entail restrictions on the use or disposal of the property. Restrictions may include requirements to purchase home insurance and mortgage insurance, or pay off outstanding debt before selling the property.
Borrower: the person borrowing who either has or is creating an ownership interest in the property.
Lender: any lender, but usually a bank or other financial institution. (In some countries, particularly the United States, Lenders may also be investors who own an interest in the mortgage through a mortgage-backed security. In such a situation, the initial lender is known as the mortgage originator, which then packages and sells the loan to investors. The payments from the borrower are thereafter collected by a loan servicer.)
Principal: the original size of the loan, which may or may not include certain other costs; as any principal is repaid, the principal will go down in size.
Interest: a financial charge for use of the lender's money.
Foreclosure or repossession: the possibility that the lender has to foreclose, repossess or seize the property under certain circumstances is essential to a mortgage loan; without this aspect, the loan is arguably no different from any other type of loan.
Completion: legal completion of the mortgage deed, and hence the start of the mortgage.
Redemption: final repayment of the amount outstanding, which may be a "natural redemption" at the end of the scheduled term or a lump sum redemption, typically when the borrower decides to sell the property. A closed mortgage account is said to be "redeemed".
Answer: Property
Mortgage
Borrower
Lender
Principal
Interest
Foreclosure or repossession
Completion
Redemption
Question: Give me a list of the 8 best west coast oysters
Answer: - Kumamoto – Washington
- Kusshi – British Colombia
- Baynes Sound – British Colombia
- Fanny Bay – British Colombia
- Drakes Bay – California
- Miyagis – British Colombia
- Marin Coast – California
- Hog Island – California
Question: What do they determine when testing different sequence labeling parsers?
Evidence: We test different sequence labeling parsers to determine whether there are any benefits in learning across representations. We compare: (i) a single-task model for constituency parsing and another one for dependency parsing, (ii) a multi-task model for constituency parsing (and another for dependency parsing) where each element of the 3-tuple is predicted as a partial label in a separate subtask instead of as a whole, (iii) different mtl models where the partial labels from a specific parsing abstraction are used as auxiliary tasks for the other one, and (iv) an mtl model that learns to produce both abstractions as main tasks.
For constituency parsing, we use the single-task model by BIBREF10 . The input is the raw sentence and the output for each token a single label of the form INLINEFORM0 = INLINEFORM1 . For dependency parsing we use the model by BIBREF11 to predict a single dependency label of the form INLINEFORM2 = INLINEFORM3 for each token.
For constituency parsing, instead of predicting a single label output of the form INLINEFORM0 , we generate three partial and separate labels INLINEFORM1 , INLINEFORM2 and INLINEFORM3 through three task-dependent feed-forward networks on the top of the stacked bilstms. This is similar to BIBREF28 . For dependency parsing, we propose in this work a mtl version too. We observed in preliminary experiments, as shown in Table TABREF14 , that casting the problem as 3-task learning led to worse results. Instead, we cast it as a 2-task learning problem, where the first task consists in predicting the head of a word INLINEFORM4 , i.e. predicting the tuple INLINEFORM5 , and the second task predicts the type of the relation INLINEFORM6 . The loss is here computed as INLINEFORM7 = INLINEFORM8 , where INLINEFORM9 is the partial loss coming from the subtask INLINEFORM10 .
We predict the partial labels from one of the parsing abstractions as main tasks. The partial labels from the other parsing paradigm are used as auxiliary tasks. The loss is computed as INLINEFORM0 = INLINEFORM1 , where INLINEFORM2 is an auxiliary loss and INLINEFORM3 its specific weighting factor. Figure FIGREF17 shows the architecture used in this and the following multi-paradigm model.
All tasks are learned as main tasks instead.
We test different sequence labeling parsers to determine whether there are any benefits in learning across representations.
Answer: Whether there are any benefits in learning across representations.
Question: Brainstorm a list of things you do in the morning before leaving for work
Answer: Check the weather, eat breakfast, make coffee, brush your teeth, put on clothes, put together your briefcase
Question: What affective-based features are used?
Evidence: We used the following affective resources relying on different emotion models.
Emolex: it contains 14,182 words associated with eight primary emotion based on the Plutchik model BIBREF10 , BIBREF11 .
EmoSenticNet(EmoSN): it is an enriched version of SenticNet BIBREF12 including 13,189 words labeled by six Ekman's basic emotion BIBREF13 , BIBREF14 .
Dictionary of Affect in Language (DAL): includes 8,742 English words labeled by three scores representing three dimensions: Pleasantness, Activation and Imagery BIBREF15 .
Affective Norms for English Words (ANEW): consists of 1,034 English words BIBREF16 rated with ratings based on the Valence-Arousal-Dominance (VAD) model BIBREF17 .
Linguistic Inquiry and Word Count (LIWC): this psycholinguistic resource BIBREF18 includes 4,500 words distributed into 64 emotional categories including positive (PosEMO) and negative (NegEMO).
We used the following affective resources relying on different emotion models.

Emolex: it contains 14,182 words associated with eight primary emotion based on the Plutchik model BIBREF10 , BIBREF11 .

EmoSenticNet(EmoSN): it is an enriched version of SenticNet BIBREF12 including 13,189 words labeled by six Ekman's basic emotion BIBREF13 , BIBREF14 .

Dictionary of Affect in Language (DAL): includes 8,742 English words labeled by three scores representing three dimensions: Pleasantness, Activation and Imagery BIBREF15 .

Affective Norms for English Words (ANEW): consists of 1,034 English words BIBREF16 rated with ratings based on the Valence-Arousal-Dominance (VAD) model BIBREF17 .

Linguistic Inquiry and Word Count (LIWC): this psycholinguistic resource BIBREF18 includes 4,500 words distributed into 64 emotional categories including positive (PosEMO) and negative (NegEMO).
Answer: affective features provided by different emotion models such as Emolex, EmoSenticNet, Dictionary of Affect in Language, Affective Norms for English Words and Linguistics Inquiry and Word Count
Question: List some of the great houses that appear in the series "A Song of Ice and Fire."
Answer: Stark, Lannister, Baratheon, Tyrell, Arryn, Martell, Karstark, Mormont.
Question: How big are the datasets?
Evidence: FLOAT SELECTED: Table 2: Number of documents in the training/dev/test sets of the in-house and ACE05 datasets.
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
FLOAT SELECTED: Table 2: Number of documents in the training/dev/test sets of the in-house and ACE05 datasets.
Our in-house dataset includes manually annotated RE data for 6 languages: English, German, Spanish, Italian, Japanese and Portuguese. It defines 56 entity types (e.g., Person, Organization, Geo-Political Entity, Location, Facility, Time, Event_Violence, etc.) and 53 relation types between the entities (e.g., AgentOf, LocatedAt, PartOf, TimeOf, AffectedBy, etc.).
The ACE05 dataset includes manually annotated RE data for 3 languages: English, Arabic and Chinese. It defines 7 entity types (Person, Organization, Geo-Political Entity, Location, Facility, Weapon, Vehicle) and 6 relation types between the entities (Agent-Artifact, General-Affiliation, ORG-Affiliation, Part-Whole, Personal-Social, Physical).
Answer: In-house dataset consists of  3716 documents 
ACE05 dataset consists of  1635 documents
Question: What is a credit score?
Answer: A credit score is a numerical representation of a person's ability to buy using credit and paying off the credit spent.  How much debt and how many credit cards a person has plays a factor in this numerical value.  This value also represents the person's credit worthiness and information from credit bureaus helps to calculate the credit score.  Credit scores are important when trying to get loans for things like cars, homes, and other things a person buys using financing.
Question: What encoder-decoder is used in their system?
Evidence: We participated in the WMT 2016 shared news translation task by building neural translation systems for four language pairs: English INLINEFORM0 Czech, English INLINEFORM1 German, English INLINEFORM2 Romanian and English INLINEFORM3 Russian. Our systems are based on an attentional encoder-decoder BIBREF0 , using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary BIBREF1 . We experimented with using automatic back-translations of the monolingual News corpus as additional training data BIBREF2 , pervasive dropout BIBREF3 , and target-bidirectional models.
Our systems are based on an attentional encoder-decoder (Bahdanau et al., 2015), using BPE subword segmentation for open-vocabulary translation with a fixed vocabulary (Sennrich et al., 2016b).
Answer: An attentional encoder-decoder (Bahdanau et al., 2015).