In this task, you will be presented with a context from an academic paper and you have to write an answerable question based on the context. Your questions can be extractive, abstractive, or yes-no questions.

Ex Input:
We focus primarily on the smaller data set of 645 hand-labeled articles provided to task participants, both for training and for validation.  We pre-trained BERT-base on the 600,000 articles without labels by using the same Cloze task BIBREF5 that BERT had originally used for pre-training. 

Ex Output:
How long is the dataset?


Ex Input:
Construction of the auxiliary sentence
For simplicity, we mainly describe our method with TABSA as an example.

We consider the following four methods to convert the TABSA task into a sentence pair classification task:

The sentence we want to generate from the target-aspect pair is a question, and the format needs to be the same. For example, for the set of a target-aspect pair (LOCATION1, safety), the sentence we generate is “what do you think of the safety of location - 1 ?"

For the NLI task, the conditions we set when generating sentences are less strict, and the form is much simpler. The sentence created at this time is not a standard sentence, but a simple pseudo-sentence, with (LOCATION1, safety) pair as an example: the auxiliary sentence is: “location - 1 - safety".

For QA-B, we add the label information and temporarily convert TABSA into a binary classification problem ( INLINEFORM0 ) to obtain the probability distribution. At this time, each target-aspect pair will generate three sequences such as “the polarity of the aspect safety of location - 1 is positive", “the polarity of the aspect safety of location - 1 is negative", “the polarity of the aspect safety of location - 1 is none". We use the probability value of INLINEFORM1 as the matching score. For a target-aspect pair which generates three sequences ( INLINEFORM2 ), we take the class of the sequence with the highest matching score for the predicted category.

The difference between NLI-B and QA-B is that the auxiliary sentence changes from a question to a pseudo-sentence. The auxiliary sentences are: “location - 1 - safety - positive", “location - 1 - safety - negative", and “location - 1 - safety - none".

After we construct the auxiliary sentence, we can transform the TABSA task from a single sentence classification task to a sentence pair classification task. As shown in Table TABREF19 , this is a necessary operation that can significantly improve the experimental results of the TABSA task.

Ex Output:
How do they generate the auxiliary sentence?


Ex Input:
In the future, we aim to use the corpus for annotation projects such as parts-of-speech tagging, named entity recognition. Furthermore, the generated word embeddings will be utilized for the automatic construction of Sindhi WordNet.

Ex Output:

Are trained word embeddings used for any other NLP task?