Add description of the dataset

README.md

@@ -5,6 +5,7 @@ language:
 arxiv:
 - 2107.12214
 - 2010.02609
+- 1911.01616
 size_categories:
 - 1K<n<10K
 task_categories:
@@ -28,3 +29,113 @@ configs:
   - split: test
     path: "data/2014/laptop/aste/test.gz.parquet"
 ---
+
+## Dataset Description
+
+### Task Summary
+
+Aspect Sentiment Triplet Extraction (ASTE) is the task of extracting the triplets of target entities, their associated sentiment, and opinion spans explaining the reason for the sentiment.
+This task is firstly proposed by (Peng et al., 2020) in the paper [Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis (In AAAI 2020)](https://arxiv.org/abs/1911.01616).
+
+For Example, given the sentence:
+
+> The screen is very large and crystal clear with amazing colors and resolution .
+
+The objective of the Aspect Sentiment Triplet Extraction (ASTE) task is to predict the triplets:
+
+> [('screen', 'large', 'Positive'), ('screen', 'clear', 'Positive'), ('colors', 'amazing', 'Positive'), ('resolution', 'amazing', 'Positive')]
+
+where a triplet consists of (target, opinion, sentiment).
+
+### Dataset Summary
+
+Sentiment analysis is increasingly viewed as a vital task both from an academic and a commercial standpoint.
+The majority of current approaches, however, attempt to detect the overall polarity of a sentence, paragraph, or text span, regardless of the entities mentioned (e.g., laptops, restaurants) and their aspects (e.g., battery, screen; food, service).
+By contrast, this task is concerned with aspect based sentiment analysis (ABSA), where the goal is to identify the aspects of given target entities and the sentiment expressed towards each aspect.
+Datasets consisting of customer reviews with human-authored annotations identifying the mentioned aspects of the target entities and the sentiment polarity of each aspect will be provided.
+
+### Dataset Source
+
+The ASTE dataset is from the [xuuuluuu/SemEval-Triplet-data](https://github.com/xuuuluuu/SemEval-Triplet-data) repository.
+
+It is based on the [Sem Eval 2014 Task 4](https://alt.qcri.org/semeval2014/task4/) dataset, with some preprocessing applied to the text.
+
+### Dataset Details
+
+The train, validation and test splits come from the ASTE dataset.
+There are the following columns:
+
+ * index
+   The ASTE and Sem Eval datasets had multiple annotations per document.
+   This dataset has a single annotation per row.
+   To make it easier to collect all annotations for a document the index can be used to group them.
+   All annotations for a given document will have the same index.
+
+ * text
+   This is the document that is annotated, either in the ASTE form or in the Sem Eval form (see below for details).
+
+ * aspect_start_index
+   The zero based character index for the first letter of the aspect term
+
+ * aspect_end_index
+   The zero based character index for the last letter of the aspect term
+
+ * aspect_term
+   The aspect term as it appears in the text
+
+ * opinion_start_index
+   The zero based character index for the first letter of the opinion term
+
+ * opinion_end_index
+   The zero based character index for the last letter of the opinion term
+
+ * opinion_term
+   The opinion term as it appears in the text
+
+ * sentiment
+   The sentiment class for the opinion about the aspect.
+   One of _negative_, _neutral_ or _positive_.
+
+The ASTE dataset involved preprocessing the SemEval text.
+This preprocessing fixed some of the spelling mistakes, for example:
+
+> Keyboard good sized and wasy to use.
+
+(easy misspelt as wasy).
+
+The preprocessing also includes tokenization of the text and then separating the tokens with whitespace, for example:
+
+> It 's just as fast with one program open as it is with sixteen open .
+
+Since the added whitespace can lead to unnatrual text I have provided two forms of the dataset.
+Subsets that end with `aste-v2` have the preprocessed text with spelling correction and additional whitespace.
+Subsets that end with `sem-eval` have the original Sem Eval text.
+
+### Citation Information
+
+```
+@misc{xu2021learning,
+      title={Learning Span-Level Interactions for Aspect Sentiment Triplet Extraction},
+      author={Lu Xu and Yew Ken Chia and Lidong Bing},
+      year={2021},
+      eprint={2107.12214},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+@misc{xu2021positionaware,
+      title={Position-Aware Tagging for Aspect Sentiment Triplet Extraction},
+      author={Lu Xu and Hao Li and Wei Lu and Lidong Bing},
+      year={2021},
+      eprint={2010.02609},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+@misc{peng2019knowing,
+      title={Knowing What, How and Why: A Near Complete Solution for Aspect-based Sentiment Analysis},
+      author={Haiyun Peng and Lu Xu and Lidong Bing and Fei Huang and Wei Lu and Luo Si},
+      year={2019},
+      eprint={1911.01616},
+      archivePrefix={arXiv},
+      primaryClass={cs.CL}
+}
+```