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.

[Q]: In our experiments, we use as input the 2210 tokenized sentences of the Stanford Sentiment Treebank test set BIBREF2 , preprocessing them by lowercasing as was done in BIBREF8 .
[A]: Which datasets are used for evaluation?

[Q]: The dataset consists of a set of polysemous words: 20 nouns, 20 verbs, and 10 adjectives and specifies 20 to 100 contexts per word, with the total of 4,664 contexts, drawn from the Open American National Corpus. Given a set of contexts of a polysemous word, the participants of the competition had to divide them into clusters by sense of the word. The contexts are manually labelled with WordNet senses of the target words, the gold standard clustering is generated from this labelling.

The task allows two setups: graded WSI where participants can submit multiple senses per word and provide the probability of each sense in a particular context, and non-graded WSI where a model determines a single sense for a word in context. In our experiments we performed non-graded WSI. We considered the most suitable sense as the one with the highest cosine similarity with embeddings of the context, as described in Section SECREF9.
[A]: How are the different senses annotated/labeled?

[Q]: We optimized our single-task baseline to get a strong baseline in order to exclude better results in multi-task learning in comparison to single-task learning only because of these two following points: network parameters suit the multi-task learning approach better and a better randomness while training in the multi-task learning. To exclude the first point, we tested different hyperparameters for the single-task baseline. We tested all the combinations of the following hyperparameter values: 256, 512, or 1024 as the sizes for the hidden states of the LSTMs, 256, 512, or 1024 as word embedding sizes, and a dropout of 30 %, 40 %, or 50 %. We used subword units generated by byte-pair encoding (BPE) BIBREF16 as inputs for our model. To avoid bad subword generation for the synthetic datasets, in addition to the training dataset, we considered the validation and test dataset for the generating of the BPE merge operations list. We trained the configurations for 14 epochs and trained every configuration three times. We chose the training with the best quality with regard to the validation F1-score to exclude disadvantages of a bad randomness. We got the best quality with regard to the F1-score with 256 as the size of the hidden states of the LSTMs, 1024 as word embedding size, and a dropout of 30 %. For the batch size, we used 64.
[A]:
What are the strong baselines you have?