Transfer Learning from LDA to BiLSTM-CNN for Offensive Language Detection in Twitter	We investigate different strategies for automatic offensive language classification on German Twitter data. For this, we employ a sequentially combined BiLSTM-CNN neural network. Based on this model, three transfer learning tasks to improve the classification performance with background knowledge are tested. We compare 1. Supervised category transfer: social media data annotated with near-offensive language categories, 2. Weakly-supervised category transfer: tweets annotated with emojis they contain, 3. Unsupervised category transfer: tweets annotated with topic clusters obtained by Latent Dirichlet Allocation (LDA). Further, we investigate the effect of three different strategies to mitigate negative effects of 'catastrophic forgetting' during transfer learning. Our results indicate that transfer learning in general improves offensive language detection. Best results are achieved from pre-training our model on the unsupervised topic clustering of tweets in combination with thematic user cluster information.

Conclusion	In this paper, we presented our neural network text classification approach for offensive language detection on the GermEval 2018 Shared Task dataset. We used a combination of BiLSTM and CNN architectures for learning. As task-specific adaptations of standard text classification, we evaluated different datasets and strategies for transfer learning, as well as additional features obtained from users addressed in tweets. The coarse-grained offensive language detection could be realized to a much better extent than the fine-grained task of separating four different categories of insults (accuracy 77.5% vs. 73.7%). From our experiments, four main messages can be drawn: The fact that our unsupervised, task-agnostic pre-training by LDA topic transfer performed best suggests that this approach will also contribute beneficially to other text classification tasks such as sentiment analysis. Thus, in future work, we plan to evaluate our approach with regard to such other tasks. We also plan to evaluate more task-agnostic approaches for transfer learning, for instance employing language modeling as a pre-training task.

For instance, Howard.2018 propose a generic transfer learning setup for text classification based on language modeling for pre-training neural models with large background corpora. To improve offensive language detection for English social media texts, a transfer learning approach was recently introduced by Felbo.2017. Their `deepmoji' approach relies on the idea to pre-train a neural network model for an actual offensive language classification task by using emojis as weakly supervised training labels. On a large collection of millions of randomly collected English tweets containing emojis, they try to predict the specific emojis from features obtained from the remaining tweet text. We will follow this idea of transfer learning to evaluate it for offensive language detection in German Twitter data together with other transfer learning strategies.

Related Work	Automatic detection of offensive language is a well-studied phenomenon for the English language. Initial works on the detection of `hostile messages' have been published already during the 1990s  . An overview of recent approaches comparing the different task definitions, feature sets and classification methods is given by Schmidt.2017. A major step forward to support the task was the publication of a large publicly available, manually annotated dataset by Yahoo research  . They provide a classification approach for detection of abusive language in Yahoo user comments using a variety of linguistic features in a linear classification model. One major result of their work was that learning text features from comments which are temporally close to the to-be-predicted data is more important than learning features from as much data as possible. This is especially important for real-life scenarios of classifying streams of comment data. In addition to token-based features, Xiang.2012 successfully employed topical features to detect offensive tweets. We will build upon this idea by employing topical data in our transfer learning setup. Transfer learning recently has gained a lot of attention since it can be easily applied to neural network learning architectures.

Introduction	User-generated content in forums, blogs, and social media not only contributes to a deliberative exchange of opinions and ideas but is also contaminated with offensive language such as threats and discrimination against people, swear words or blunt insults. The automatic detection of such content can be a useful support for moderators of public platforms as well as for users who could receive warnings or would be enabled to filter unwanted content. Although this topic now has been studied for more than two decades, so far there has been little work on offensive language detection for German social media content. Regarding this, we present a new approach to detect offensive language as defined in the shared task of the GermEval 2018 workshop. For our contribution to the shared task, we focus on the question how to apply transfer learning for neural network-based text classification systems. In Germany, the growing interest in hate speech analysis and detection is closely related to recent political developments such as the increase of right-wing populism, and societal reactions to the ongoing influx of refugees seeking asylum  .

Evaluation	Since there is no prior state-of-the-art for the GermEval Shared Task 2018 dataset, we evaluate the performance of our neural model compared to the baseline SVM architecture. We further compare the different tasks and strategies for transfer learning introduced above and provide some first insights on error analysis.

Text Classification	In the following section, we describe one linear classification model in combination with specifically engineered features, which we use as a baseline for the classification task. We further introduce a neural network model as a basis for our approach to transfer learning. This model achieves the highest performance for offensive language detection, as compared to our baseline.

Transfer Learning	As mentioned earlier, we investigate potential strategies for transfer learning to achieve optimal performance. For this, we compare three different methods to pre-train our model with background data sets. We also compare three different strategies to combat `catastrophic forgetting' during training on the actual target data.

Background Knowledge	Since the provided dataset for offensive language detection is rather small, we investigate the potential of transfer learning to increase classification performance. For this, we use the following labeled as well as unlabeled datasets. A recently published resource of German language social media data has been published by Schabus2017. Among other things, the dataset contains 11,773 labeled user comments posted to the Austrian newspaper website `Der Standard'. Comments have not been annotated for offensive language, but for categories such as positive/negative sentiment, off-topic, inappropriate or discriminating. As a second resource, we use a background corpus of German tweets that were collected using the Twitter streaming API from 2011 to 2017. Since the API provides a random fraction of all tweets (1%), language identification is performed using `langid.py'  to filter for German tweets. For all years combined, we obtain about 18 million unlabeled German tweets from the stream, which can be used as a large, in-domain background corpus. For a transfer learning setup, we need to specify a task to train the model and prepare the corresponding dataset. We compare the following three methods.

Transfer Learning Strategies	Once the neural model has been pre-trained on the above-specified targets and corresponding datasets, we can apply it for learning our actual target task. For this, we need to remove the final prediction layer of the pre-trained model (i.e. Layer 4 in Fig. FIGREF15 ), and add a new dense layer for prediction of one of the actual label sets (two for Task 1, four for Task 2). The training for the actual GermEval tasks is conducted with batch size 32 for up to 50 epochs. To prevent the aforementioned effect of forgetting pre-trained knowledge during this task-specific model training, we evaluate three different strategies. In Howard.2018, gradual unfreezing of pre-trained model weights is proposed as one strategy to mitigate forgetting. The basic idea is to initially freeze all pre-trained weights of the neural model and keep only the newly added last layer trainable (i.e. Layer 4 in Fig. FIGREF15 ). After training that last layer for one epoch on the GermEval training data, the next lower frozen layer is unfrozen and training will be repeated for another epoch.

This will be iterated until all layers (4 to 1) are unfrozen. Following the approach of Felbo.2017, we do not iteratively unfreeze all layers of the model, but only one at a time. First, the newly added final prediction layer is trained while all other model weights remain frozen. Training is conducted for up to 50 epochs. The best performing model during these epochs with respect to our validation set is then used in the next step of fine-tuning the pre-trained model layers. For the bottom-up strategy, we unfreeze the lowest layer (1) containing the most general knowledge first, then we continue optimization with the more specific layers (2 and 3) one after the other. During fine-tuning of each single layer, all other layers remain frozen and training is performed for 50 epochs selecting the best performing model at the end of each layer optimization. In a final round of fine-tuning, all layers are unfrozen.

Emojis are then removed from the actual tweets and treated as a label to predict by the neural model. This results in a multi-class classification task to predict the right emoji out of 1,297 different ones. Our training dataset contains 1,904,330 training examples. As a final method, we create a training data set for transfer learning in a completely unsupervised manner. For this, we compute an LDA clustering with INLINEFORM0 topics on 10 million tweets sampled from 2016 and 2017 from our Twitter background corpus containing at least two meaningful words (i.e. alphanumeric sequences that are not stopwords, URLs or user mentions). Tweets also have been deduplicated before sampling. From the topic-document distribution of the resulting LDA model, we determined the majority topic id for each tweet as a target label for prediction during pre-training our neural model. Pre-training of the neural model was conducted on the 10 million tweets with batch size 128 for 10 epochs.

Additionally, we employ a polarity lexicon and perform lexical expansion on it to obtain new entries from our in-domain background corpus that are weighted on a `positive–negative' continuum. Lexical expansion is based on distributional word similarity as described in Kumar.2016.

BiLSTM-CNN for Text Classification	For transfer learning, we rely on a neural network architecture implemented in the Keras framework for Python. Our model (see Fig. FIGREF15 ) combines a bi-directional LSTM layer  with 100 units followed by three parallel convolutional layers (CNN), each with a different kernel size INLINEFORM0 , and a filter size 200. The outputs of the three CNN blocks are max-pooled globally and concatenated. Finally, features encoded by the CNN blocks are fed into a dense layer with 100 units, followed by the prediction layer. Except for this final layer which uses Softmax activation, we rely on LeakyReLU activation  for the other model layers. For regularization, dropout is applied to the LSTM layer and to each CNN block after global max-pooling (dropout rate 0.5). For training, we use the Nesterov Adam optimization and categorical cross-entropy loss with a learning rate of 0.002. The intuition behind this architecture is that the recurrent LSTM layer can serve as a feature encoder for general language characteristics from sequences of semantic word embeddings.

GermEval 2018 Shared Task	Organizers of GermEval 2018 provide training and test datasets for two tasks. Task 1 is a binary classification for deciding whether or not a German tweet contains offensive language (the respective category labels are `offense' and `other'). Task 2 is a multi-class classification with more fine-grained labels sub-categorizing the same tweets into either `insult', `profanity', `abuse', or `other'. The training data contains 5,008 manually labeled tweets sampled from Twitter from selected accounts that are suspected to contain a high share of offensive language. Manual inspection reveals a high share of political tweets among those labeled as offensive. These tweets range from offending single Twitter users, politicians and parties to degradation of whole social groups such as Muslims, migrants or refugees. The test data contains 3,532 tweets. To create a realistic scenario of truly unseen test data, training and test set are sampled from disjoint user accounts. No standard validation set is provided for the task. To optimize hyper-parameters of our classification models and allow for early stopping to prevent the neural models from overfitting, we created our own validation set.

This proceeding is similar the one described above, but inverts the order of unfreezing single layers from top to bottom sequentially fine-tuning layers 4, 3, 2, 1 individually, and all together in a final round. All strategies are compared to the baseline of no freezing of model weights, but training all layers at once directly after pre-training with one of the three transfer datasets.

As introduced above, the `One Million Post' corpus provides annotation labels for more than 11,000 user comments. Although there is no directly comparable category capturing `offensive language' as defined in the shared task, there are two closely related categories. From the resource, we extract all those comments in which a majority of the annotators agree that they contain either `inappropriate' or `discriminating' content, or none of the aforementioned. We treat the first two cases as examples of `offense' and the latter case as examples of `other'. This results in 3,599 training examples (519 offense, 3080 other) from on the `One Million Post' corpus. We conduct pre-training of the neural model as a binary classification task (similar to the Task 1 of GermEval 2018) Following the approach of Felbo.2017, we constructed a weakly-supervised training dataset from our Twitter background corpus. From all tweets posted between 2013 and 2017, we extract those containing at least one emoji character. In the case of several emojis in one tweet, we duplicate the tweet for each unique emoji type.

In a crowd-sourcing experiment on `hate speech' annotation, Ross.2016 achieved only very low inter-rater agreement between annotators. Offensive language is probably somewhat easier to achieve agreement on, but still sentence-level semantics and context or `world knowledge' remains important. Third, there is a lack of a common definition of the actual phenomenon to tackle. Published studies focus on `hostile messages', `flames', `hate speech', `discrimination', `abusive language', or `offensive language'. Although certainly overlapping, each of these categories has been operationalized in a slightly different manner. Since category definitions do not match properly, publicly available annotated datasets and language resources for one task cannot be used directly to train classifiers for any respective other task.

Content analysis studies such as InstituteforStrategicDialogue.2018 have shown that a majority of hate speech comments in German Facebook is authored by a rather small group of very active users (5% of all accounts engaging in hate speech). The findings suggest that already such small groups are able to severely disturb social media debates for large audiences. From the perspective of natural language processing, the task of automatic detection of offensive language in social media is complex due to three major reasons. First, we can expect `atypical' language data due to incorrect spellings, false grammar and non-standard language variations such as slang terms, intensifiers, or emojis/emoticons. For the automatic detection of offensive language, it is not quite clear whether these irregularities should be treated as `noise' or as a signal. Second, the task cannot be reduced to an analysis of word-level semantics only, e.g. spotting offensive keyterms in the data. Instead, the assessment of whether or not a post contains offensive language can be highly dependent on sentence and discourse level semantics, as well as subjective criteria.

The convolutional layers on top of this can then encode category related features delivered by the LSTM while the last dense layers finally fine-tune highly category-specific features for the actual classification task. As input, we feed 300-dimensional word embeddings obtained from fastText  into our model. Since fastText also makes use of sub-word information (character n-grams), it has the great advantage that it can provide semantic embeddings also for words that have not been seen during training the embedding model. We use a model pre-trained with German language data from Wikipedia and Common Crawl provided by mikolov2018advances. First, we unify all Twitter-typical user mentions (`@username') and URLs into a single string representation and reduce all characters to lower case. Then, we split tweets into tokens at boundaries of changing character classes. As an exception, sequences of emoji characters are split into single character tokens. Finally, for each token, an embedding vector is obtained from the fastText model. For offensive language detection in Twitter, users addressed in tweets might be an additional relevant signal. We assume it is more likely that politicians or news agencies are addressees of offensive language than, for instance, musicians or athletes.

SVM baseline:	The baseline classifier uses a linear Support Vector Machine  , which is suited for a high number of features. We use a text classification framework for German  that has been used successfully for sentiment analysis before. We induce token features based on the Twitter background corpus. Because tweets are usually very short, they are not an optimal source to obtain good estimates on inverse document frequencies (IDF). To obtain a better feature weighting, we calculate IDF scores based on the Twitter corpus combined with an in-house product review dataset (cf. ibid.). From this combined corpus, we compute the IDF scores and 300-dimensional word embeddings  for all contained features. Following Ruppert2017, we use the IDF scores to obtain the highest-weighted terms per category in the training data. Here, we obtain words like Staatsfunk, Vasall (state media, vassal) or deutschlandfeindlichen (Germany-opposing) for the category `abuse' and curse words for `insult'. Further, IDF scores are used to weight the word vectors of all terms in a tweet.

To make use of such information, we obtain a clustering of user ids from our Twitter background corpus. From all tweets in our stream from 2016 or 2017, we extract those tweets that have at least two @-mentions and all of the @-mentions have been seen at least five times in the background corpus. Based on the resulting 1.8 million lists of about 169,000 distinct user ids, we compute a topic model with INLINEFORM0 topics using Latent Dirichlet Allocation  . For each of the user ids, we extract the most probable topic from the inferred user id-topic distribution as cluster id. This results in a thematic cluster id for most of the user ids in our background corpus grouping together accounts such as American or German political actors, musicians, media websites or sports clubs (see Table TABREF17 ). For our final classification approach, cluster ids for users mentioned in tweets are fed as a second input in addition to (sub-)word embeddings to the penultimate dense layer of the neural network model.

For this, we used the last 808 examples from the provided training set. The remaining first 4,200 examples were used to train our models.

Question: By how much does transfer learning improve performance on this task?
Answer: In task 1 best transfer learning strategy improves F1 score by 4.4% and accuracy score by 3.3%, in task 2 best transfer learning strategy improves F1 score by 2.9% and accuracy score by 1.7%