Slide Title: Motivations
Original Content	[s1] Insufficient or even unavailable training data of emerging classes is a big challenge in real-world text classification. [s2] Zero-shot text classification recognising text documents of classes that have never been seen in the learning stage. [s3] In this paper, we propose a two-phase framework together with data augmentation and feature augmentation to solve this problem.
BARTKeyword	[s1] Zero-shot learning (ZSL) for text classification aims to classify documents of classes which are absent from the learning stage. [s2] The number of topics on social media is growing rapidly, and the classification models are required to recognise the text of the new topics using only general information (e.g., descriptions of the topics) since labelled training instances are unfeasible.
BARTSumm	[s1] Zero-shot learning (ZSL) for text classification aims to classify documents of classes which are absent from the learning stage. [s2] Humans are able to learn new concepts by transferring knowledge from known to unknown domains based on high-level descriptions and semantic representations (Thrun and Pratt, 1998). [s3] ZSL is expected to exploit supportive semantic knowledge (e.g., class descriptions, relations among classes, and external domain knowledge) to generally infer the features of unseen classes using patterns learned from seen classes.
BertSummExt	[s1] The most widely used one is semantic attributes of classes such as visual concepts (e.g., colours, shapes) and semantic properties (e.g., behaviours, functions) (Lampert et al., 2009; Zhao et al., 2018). [s2] Hence, when the names of unseen classes are known in the inference stage, we try to introduce them to the classifiers in Phase 1 via augmented data so they can learn to reject the instances likely from unseen classes. [s3] So far, three main types of semantic knowledge have been employed in general zero-shot scenarios.
Slide Title: Our Proposed Framework Overview
Original Content	[s1] We integrate four kinds of semantic knowledge into our framework. [s2] Data augmentation technique helps the classifiers be aware of the existence of unseen classes without accessing their real data. [s3] Feature augmentation provides additional information which relates the document and the unseen classes to generalise the zero-shot reasoning.
BARTKeyword	[s1] We propose a novel deep learning based Two phase framework to tackle the zero-shot text classification problem. [s2] The first phase predicts whether an input document comes from seen or unseen classes. [s3] We also apply a data augmentation technique in this phase to help the classifiers be aware of the existence of unseen classes without accessing their real data. [s4] Then the second phase, fine-grained classification, finally specifies the class of the input document.
BARTSumm	[s1] We propose a novel feature augmentation technique called topic translation to detect documents from unseen classes by topic-translation from the documents of seen classes in the training dataset. [s2] In the document level, we follow Algorithm 1 to translate a document of class c into the topic of another class c. [s3] To explain, we translate all nouns, verbs, adjectives, and adverbs in the given document to the target class, word-by-word, using the word-level analogy.
BertSummExt	[s1] All notations are defined in section 2.1-2.2. [s2] For example, solving the analogy company::firm :: village:?[s3] The fine-grained phase finally decides the class yi.
Slide Title: Experiments data sets
Original Content	[s1] Spanish and Dutch: CoNLL 2002 [s2] Russian: LDC2016E95 (Russian Representative Language Pack) [s3] Chechen: TAC KBP 2017 10-Language EDL Pilot Evaluation Source Corpus [s4] Part-of-speech Tagging: CoNLL 2017 (Universal Dependencies)
BARTKeyword	[s1] We use the following data sets: Dutch (NLD) and Spanish (ESP) data from the CoNLL 2002 shared task (Tjong Kim Sang, 2002). [s2] English (ENG) and English (ENG), Russian (RUS) Data from LDC2016E95 (Russian Representative Language Pack), and Chechen (CHE) Data from TAC KBP 2017 10-Language EDL Pilot Evaluation Source Corpus 4.
BARTSumm	[s1] LSTM-CRFs-based models are well-suited for multi-lingual multi-task learning for three reasons: (1) They learn features from word and character embeddings and therefore require little feature engineering; (2) As the input and output of each layer in a neural network are abstracted as vectors, it is fairly straightforward to share components between neural models; (3) Character Embeddings can serve as a bridge to transfer morphological and semantic information between languages with identical or similar scripts, without requiring crosslingual dictionaries or parallel sentences.
BertSummExt	[s1] Experiments Data Sets For Name Tagging, we use the following data sets: Dutch (NLD) and Spanish (ESP) data from the CoNLL 2002 shared task (Tjong Kim Sang, 2002), English (ENG) data from the CoNLL 2003 shared task (Tjong Kim Sang and De Meulder, 2003), Russian (RUS) data from LDC2016E95 (Russian Representative Language Pack), and Chechen (CHE) data from TAC KBP 2017 10-Language EDL Pilot Evaluation Source Corpus 4. [s2] In this data set, each token is annotated with two POS tags, UPOS (universal POS tag) and XPOS (language-specific POS tag). [s3] English, Spanish, and Dutch embeddings are trained on corresponding Wikipedia articles (2017-12-20 dumps).