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@@ -59,15 +59,15 @@ of discourse analysis and coherence.
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  1. **Sentence Positioning**
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  - **Datasets Sources**: Arxiv, Wikipedia, Rocstory
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- - **Description**: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations $x_i$. As input to the classfier we include $x_1$ and the contcatination of $x_1 - x_i$ for all $i$: \\([x_1, x_1 - x_2, x_1-x_3,x_1-x_4,x_1-x_5]\\)
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  2. **Binary Sentence Ordering**
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  - **Datasets Sources**: Arxiv, Wikipedia, Rocstory
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- - **Description**: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: $[x_1, x_2, x_1-x_2]$
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  3. **Discourse Coherence**
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  - **Datasets Sources**: Ubuntu IRC channel, Wikipedia
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- - **Description**: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: $[x_1, x_2, x_3, x_4, x_5, x_6]$
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  4. **Sentence Section Prediction**
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  - **Datasets Sources**: Constructed from PeerRead
 
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  1. **Sentence Positioning**
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  - **Datasets Sources**: Arxiv, Wikipedia, Rocstory
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+ - **Description**: Determine the correct placement of a sentence within a given context of five sentences. To form the input when training classifiers encode the five sentences to vector representations \\(x_i\\). As input to the classfier we include \\(x_1\\) and the contcatination of \\(x_1 - x_i\\) for all \\(i\\): \\([x_1, x_1 - x_2, x_1-x_3,x_1-x_4,x_1-x_5]\\)
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  2. **Binary Sentence Ordering**
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  - **Datasets Sources**: Arxiv, Wikipedia, Rocstory
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+ - **Description**: Determining whether two sentences are in the correct consecutive order, identifying the more coherent structure. To form the input when training classifiers, we concatenate the embeddings of both sentences with their element-wise difference: \\([x_1, x_2, x_1-x_2]\\)
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  3. **Discourse Coherence**
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  - **Datasets Sources**: Ubuntu IRC channel, Wikipedia
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+ - **Description**: Determine whether a sequence of six sentences form a coherent paragraph. To form the input when training classifiers, encode all sentences to vector representations and concatenate all of them: \\([x_1, x_2, x_3, x_4, x_5, x_6]\\)
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  4. **Sentence Section Prediction**
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  - **Datasets Sources**: Constructed from PeerRead