Code-switching is still an understudied phenomenon in natural language processing mainly because of two related challenges: it lacks annotated data, and it combines a vast diversity of low-resource languages. Despite the language diversity, many code-switching scenarios occur in language pairs, and English is often a common factor among them. In the first part of this paper, we use transfer learning from English to English-paired code-switched languages for the language identification (LID) task by applying two simple yet effective techniques: 1) a hierarchical attention mechanism that enhances morphological clues from character n-grams, and 2) a secondary loss that forces the model to learn n-gram representations that are particular to the languages involved. We use the bottom layers of the ELMo architecture to learn these morphological clues by essentially recognizing what is and what is not English. Our approach outperforms the previous state of the art on Nepali-English, Spanish-English, and Hindi-English datasets. In the second part of the paper, we use our best LID models for the tasks of Spanish-English named entity recognition and Hindi-English part-of-speech tagging by replacing their inference layers and retraining them. We show that our retrained models are capable of using the code-switching information on both tasks to outperform models that do not have such knowledge.
Named entity recognition is one of the core tasks in NLP. Although many improvements have been made on this task during the last years, the state-of-the-art systems do not explicitly take into account the recursive nature of language. Instead of only treating the text as a plain sequence of words, we incorporate a linguistically-inspired way to recognize entities based on syntax and tree structures. Our model exploits syntactic relationships among words using a Tree-LSTM guided by dependency trees. Then, we enhance these features by applying relative and global attention mechanisms. On the one hand, the relative attention detects the most informative words in the sentence with respect to the word being evaluated. On the other hand, the global attention spots the most relevant words in the sequence. Lastly, we linearly project the weighted vectors into the tagging space so that a conditional random field classifier predicts the entity labels. Our findings show that the model detects words that disclose the entity types based on their syntactic roles in a sentence (e.g., verbs such as speak and write are attended when the entity type is PERSON, whereas meet and travel strongly relate to LOCATION). We confirm our findings and establish a new state of the art on two datasets.
In recent years, abusive behavior has become a serious issue in online social networks. In this paper, we present a new corpus from a semi-anonymous social media platform, which contains the instances of offensive and neutral classes. We introduce a single deep neural architecture that considers both local and sequential information from the text in order to detect abusive language. Along with this model, we introduce a new attention mechanism called emotion-aware attention. This mechanism utilizes the emotions behind the text to find the most important words within that text. We experiment with this model on our dataset and later present the analysis. Additionally, we evaluate our proposed method on different corpora and show new state-of-the-art results with respect to offensive language detection.
This paper considers the problem of characterizing stories by inferring attributes like theme and genre using the written narrative and user reviews. We experiment with a multi-label dataset of narratives representing the story of movies and a tagset representing various attributes of stories. To identify the story attributes, we propose a hierarchical representation of narratives that improves over the traditional feature-based machine learning methods as well as sequential representation approaches. Finally, we demonstrate a multi-view method for discovering story attributes from user opinions in reviews that are complementary to the gold standard data set.
The film culture has grown tremendously in recent years. The large number of streaming services put films as one of the most convenient forms of entertainment in today's world. Films can help us learn and inspire societal change. But they can also negatively affect viewers. In this paper, our goal is to predict the suitability of the movie content for children and young adults based on scripts. The criterion that we use to measure suitability is the MPAA rating that is specifically designed for this purpose. We propose an RNN based architecture with attention that jointly models the genre and the emotions in the script to predict the MPAA rating. We achieve 78% weighted F1-score for the classification model that outperforms the traditional machine learning method by 6%.
In the third shared task of the Computational Approaches to Linguistic Code-Switching (CALCS) workshop, we focus on Named Entity Recognition (NER) on code-switched social-media data. We divide the shared task into two competitions based on the English-Spanish (ENG-SPA) and Modern Standard Arabic-Egyptian (MSA-EGY) language pairs. We use Twitter data and 9 entity types to establish a new dataset for code-switched NER benchmarks. In addition to the CS phenomenon, the diversity of the entities and the social media challenges make the task considerably hard to process. As a result, the best scores of the competitions are 63.76% and 71.61% for ENG-SPA and MSA-EGY, respectively. We present the scores of 9 participants and discuss the most common challenges among submissions.
Named Entity Recognition for social media data is challenging because of its inherent noisiness. In addition to improper grammatical structures, it contains spelling inconsistencies and numerous informal abbreviations. We propose a novel multi-task approach by employing a more general secondary task of Named Entity (NE) segmentation together with the primary task of fine-grained NE categorization. The multi-task neural network architecture learns higher order feature representations from word and character sequences along with basic Part-of-Speech tags and gazetteer information. This neural network acts as a feature extractor to feed a Conditional Random Fields classifier. We were able to obtain the first position in the 3rd Workshop on Noisy User-generated Text (WNUT-2017) with a 41.86% entity F1-score and a 40.24% surface F1-score.
Recognizing named entities in a document is a key task in many NLP applications. Although current state-of-the-art approaches to this task reach a high performance on clean text (e.g. newswire genres), those algorithms dramatically degrade when they are moved to noisy environments such as social media domains. We present two systems that address the challenges of processing social media data using character-level phonetics and phonology, word embeddings, and Part-of-Speech tags as features. The first model is a multitask end-to-end Bidirectional Long Short-Term Memory (BLSTM)-Conditional Random Field (CRF) network whose output layer contains two CRF classifiers. The second model uses a multitask BLSTM network as feature extractor that transfers the learning to a CRF classifier for the final prediction. Our systems outperform the current F1 scores of the state of the art on the Workshop on Noisy User-generated Text 2017 dataset by 2.45% and 3.69%, establishing a more suitable approach for social media environments.
Domain-specific community question answering is becoming an integral part of professions. Finding related questions and answers in these communities can significantly improve the effectiveness and efficiency of information seeking. Stack Overflow is one of the most popular communities that is being used by millions of programmers. In this paper, we analyze the problem of predicting knowledge unit (question thread) relatedness in Stack Overflow. In particular, we formulate the question relatedness task as a multi-class classification problem with four degrees of relatedness. We present a large-scale dataset with more than 300K pairs. To the best of our knowledge, this dataset is the largest domain-specific dataset for Question-Question relatedness. We present the steps that we took to collect, clean, process, and assure the quality of the dataset. The proposed dataset Stack Overflow is a useful resource to develop novel solutions, specifically data-hungry neural network models, for the prediction of relatedness in technical community question-answering forums. We adopt a neural network architecture and a traditional model for this task that effectively utilize information from different parts of knowledge units to compute the relatedness between them. These models can be used to benchmark novel models, as they perform well in our task and in a closely similar task.