What Gets Echoed? Understanding the “Pointers” in Explanations of Persuasive Arguments
Table 10: Parameters tuned on validation dataset containing 5k instances.
['[EMPTY]', 'Without features', 'With features']
We formulate an abstractive summarization task using an OP concatenated with the PC as a source, and the explanation as target. We train two models, one with the features described above, and one without. A shared vocabulary of 50k words is constructed from the training set by setting the maximum encoding length to 500 words. We set the maximum decoding length to 100. We use a pointer generator network with coverage for generating explanations, using a bidirectional LSTM as an encoder and a unidirectional LSTM as a decoder. Both use a 256-dimensional hidden state. The parameters of this network are tuned using a validation set of five thousand instances.