SlowGuess/ACL_2020_OCR huggingface dataset:
- 1-3 word grams as binary vectors, count vectors, and tfidf weighted vectors. Word grams must appear in at least  $1\%$  of posts and no more than  $95\%$  of posts.
1-6 character grams as count vectors. Character grams must appear in at least  $10\%$  of posts and no more than  $95\%$  of posts.
- 1-3 Part-Of-Speech grams as count vectors. The Part-Of-Speech tags were generated using the NLTK library (Loper and Bird, 2002). The POS tags were used in two formats, with the tags concatenated to their corresponding word (e.g. word1_POS1 word2_POS2 ...) and with the POS tags appended to the end of the sentence (e.g. word1 word2 ...word_N POS1 POS2 ...POS_N).
Topic Features:
- Topic membership of each post. LDA topic modeling was run on the entire dataset. Different numbers of topics were tested and their performance was judged using silhouette score. The best performing model had two topics. Word Embedding Features:
100-dimensional word embedding sums for each post. The word embeddings were trained using Mallet (McCallum, 2002). Similarity Features:
- The cosine similarity between the summed word embeddings for the target post and its parent post.
Lexical Features:
- The ratio of positive words to all words, ratio of negative words to all words, sum count of positive words, sum count of negative words, and the positive and negative count for each POS tag for the MPQA (Wilson et al., 2005) and SentiWordNet (Baccianella et al., 2010) lexicons.
- The ratio of positive words to all words, ratio of negative words to all words, sum count of positive words, sum count of negative words for the Hu Liu Lexicon (Hu and Liu, 2004).
- The sum score, maximum score, positive sum, and negative sum for sentiment tokens from the NRC lexicon (Mohammad et al., 2013).
In their original paper, Mourad et al. (2018), used the reliefF (Kononenko et al., 1997) features selection technique to select the 50 most important features. We tested using the top 50 features selected using reliefF and reducing the feature size to 50 using Principal Component Analysis (PCA), as well as using the full fea-ture set. We found that the full feature set (2855 total) performed significantly better than the reliefF and PCA feature sets. We used the full feature set in our final model.
A.1.2 pkudblab-PIP Training
The pkudblab-PIP model used the following input sizes:
- Word Embedding Size  $(d)$ : 300.
Maximum Sentence Length  $(|s|)$ : 150. Posts longer than 150 words were truncated from the beginning and posts less than 150 words were padded at the end.
- Total number of filters: 300. 100 for each window size: 3, 4, and 5.
The model was trained using a batch size of 64, a drop-out rate of  $50\%$ , and used an Adam optimizer (Kingma and Ba, 2014).
A.1.3 T-PAN-PIP Training
The T-PAN-PIP model used the following input sizes:
- Word Embedding Size  $(d)$ : 300.
Maximum Sentence Length  $(|s|)$ : 150. Posts longer than 150 words were truncated from the beginning and posts less than 150 words were padded at the end.
- LSTM hidden units: 256 total (128 for each direction).
The model was trained using a batch size of 64 and used an Adam optimizer.
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