# Improving Sentence Representations via Component Focusing
## 4 Experiment  
### Datasets and Tasks  
Two widely used datasets are employed in the experiment: Sentences Involving Compositional Knowledge (SICK) dataset and the Semantic Textual Similarity Benchmark (STS-B) dataset.  
The SICK dataset was published in SemEval-2014 [39] Task 1 and is composed of two tasks: the Entailment Classification task SICK-E (entailment) and Semantic Textual Similarity task SICK-R  
\begin{table}
\begin{tabular}{l l l l} \hline \hline
**subj** & **obj** & **VB** & **NN** \\ \hline
**csubj**: clausal subject & **dobj**: direct object & **VB**: verb, base form & **NN**: noun, singular or mass \\
**csubjpass**: clausal passive subject & **iobj**: indirect object & **VBD**: verb, past tense & **NN**: noun, plural \\
**nsubj**: nominal subject & **pobj**: object of a proposition & **VBG**: verb, gewund/present & **NNP**: noun, proper noun, \\
**nsubjpass**: passive nominal subject & & **VBN**: verb, past participle & **NNP**: proper noun, plural \\
**nsubj**: controlling subject & & **VBP**: verb, non-3d ps. sing. present & \\  & & **VBZ**: verb, 3d ps. sing. present & \\ \hline \hline \end{tabular}
\end{table}
Table 2: All the dependency relation including subj and obj, parts of speech including VB (verb) and NN (noun) defined by Standford Parser and what they stand for.  
\begin{table}
\begin{tabular}{l l} \hline \hline
**An Asian Woman in a Crowd Is Not Carrying a Black Bag** & **A Man Attacks a Woman** \\ \hline (('carrying’, 'VBG’), 'nsubj', ('woman’, 'NN’)) & ((’man’, 'NN’), ‘det’, (’A’, 'DT’)) \\ ((’woman’, 'NN’), 'det’, (’An’, 'DT’)) & ((’man’, 'NN’), ‘dep’, (‘attacks’, 'NN’)) \\ ((’woman’, 'NN’), 'amod’, (’Asian’, 'JJ’)) & ((’man’, 'NN’), ‘dep’, (‘woman’, 'NN’)) \\ ((’woman’, 'NN’), 'nmod’, (’crowd’, 'NN’)) & ((’woman’, 'NN’), ‘det’, (’a’, 'DT’)) \\ ((’crowd’, 'NN’), 'case’, ('in’, 'IN’)) & ((’man’, 'NN’), ‘det’, (’A’, 'DT’)) \\ ((’crowd’, 'NN’), 'det’, (’a’, 'DT’)) & \\ ((’carrying’, 'VBG’), 'aux’, (’is’, 'VBZ’)) & \\ ((’carrying’, 'VBG’), 'neg’, (’not’, 'RB’)) & \\ (’carrying’, 'VBG’), 'dobj’, (bag’, 'NN’)) & \\ ((’bag’, 'NN’), ‘det’, (’a’, 'DT’) & \\ ((’bag’, 'NN’), 'amod’, (’black’, 'JJ’)) & \\ \hline \hline \end{tabular}
\end{table}
Table 3: Results produced by the Stanford Parser.  
(relatedness). The dataset contains 9927 (4500 for train set/4927 for test set/500 for development set) pairs of sentences. Each sentence pair is annotated with a relatedness label \(\in\) [1,5] corresponding to the average relatedness judged by ten different individuals; each of the SICK sentence pairs has also been labeled as one of three classes: entailment, contradiction, or neutral, which are to be predicted for the test examples.  
The STS brings together the English data from the SemEval Semantic Textual Similarity tasks between 2012 and 2017. It was published to provide a standard benchmark to evaluate various semantic representation models. The dataset includes 8628 sentences pairs that are divided into train (5749), development (1500), and test (1379). STS aims to measure the degree of equivalence in meaning or semantics between a pair of sentences. The evaluation consists of human annotated English sentence pairs, scored on a scale of 0 to 5 to quantify the similarity of meaning, with 0 being the least, and 5 the most similar.  
We provide a description and sample instances of these datasets and tasks in Table 4.