Symbolic inductive bias for visually grounded learning of spoken language

A widespread approach to processing spoken language is to first automatically transcribe it into text. An alternative is to use an end-to-end approach: recent works have proposed to learn semantic embeddings of spoken language from images with spoken captions, without an intermediate transcription step. We propose to use multitask learning to exploit existing transcribed speech within the end-to-end setting. We describe a three-task architecture which combines the objectives of matching spoken captions with corresponding images, speech with text, and text with images. We show that the addition of the speech/text task leads to substantial performance improvements on image retrieval when compared to training the speech/image task in isolation. We conjecture that this is due to a strong inductive bias transcribed speech provides to the model, and offer supporting evidence for this.Comment: ACL 201

A Correlational Encoder Decoder Architecture for Pivot Based Sequence Generation

Interlingua based Machine Translation (MT) aims to encode multiple languages into a common linguistic representation and then decode sentences in multiple target languages from this representation. In this work we explore this idea in the context of neural encoder decoder architectures, albeit on a smaller scale and without MT as the end goal. Specifically, we consider the case of three languages or modalities X, Z and Y wherein we are interested in generating sequences in Y starting from information available in X. However, there is no parallel training data available between X and Y but, training data is available between X & Z and Z & Y (as is often the case in many real world applications). Z thus acts as a pivot/bridge. An obvious solution, which is perhaps less elegant but works very well in practice is to train a two stage model which first converts from X to Z and then from Z to Y. Instead we explore an interlingua inspired solution which jointly learns to do the following (i) encode X and Z to a common representation and (ii) decode Y from this common representation. We evaluate our model on two tasks: (i) bridge transliteration and (ii) bridge captioning. We report promising results in both these applications and believe that this is a right step towards truly interlingua inspired encoder decoder architectures.Comment: 10 page

Hierarchical Character-Word Models for Language Identification

Social media messages' brevity and unconventional spelling pose a challenge to language identification. We introduce a hierarchical model that learns character and contextualized word-level representations for language identification. Our method performs well against strong base- lines, and can also reveal code-switching

PersoNER: Persian named-entity recognition

© 1963-2018 ACL. Named-Entity Recognition (NER) is still a challenging task for languages with low digital resources. The main difficulties arise from the scarcity of annotated corpora and the consequent problematic training of an effective NER pipeline. To abridge this gap, in this paper we target the Persian language that is spoken by a population of over a hundred million people world-wide. We first present and provide ArmanPerosNERCorpus, the first manually-annotated Persian NER corpus. Then, we introduce PersoNER, an NER pipeline for Persian that leverages a word embedding and a sequential max-margin classifier. The experimental results show that the proposed approach is capable of achieving interesting MUC7 and CoNNL scores while outperforming two alternatives based on a CRF and a recurrent neural network

HindiPersonalityNet: Personality Detection in Hindi Conversational Data using Deep Learning with Static Embedding

Personality detection along with other behavioural and cognitive assessment can essentially explain why people act the way they do and can be useful to various online applications such as recommender systems, job screening, matchmaking, and counselling. Additionally, psychometric NLP relying on textual cues and distinctive markers in writing style within conversational utterances reveal signs of individual personalities. This work demonstrates a text-based deep neural model, HindiPersonalityNet of classifying conversations into three personality categories {ambivert, extrovert, introvert} for detecting personality in Hindi conversational data. The model utilizes GRU with BioWordVec embeddings for text classification and is trained/tested on a novel dataset, शख्सियत (pronounced as Shakhsiyat) curated using dialogues from an Indian crime-thriller drama series, Aarya. The model achieves an F1-score of 0.701 and shows the potential for leveraging conversational data from various sources to understand and predict a person's personality traits. It exhibits the ability to capture semantic as well as long-distance dependencies in conversations and establishes the effectiveness of our dataset as a benchmark for personality detection in Hindi dialogue data. Further, a comprehensive comparison of various static and dynamic word embedding is done on our standardized dataset to ascertain the most suitable embedding method for personality detection

Hate Speech Detection in a mix of English and Hindi-English (Code-Mixed) Tweets

With the increasing usage of social networking platforms seen over recent years, there has been an extensive rise in hate speech usage between the users. Hence, Government and social media platforms face lots of responsibility and challenges to control, detect and eliminate massively growing hateful content as early as possible to prevent future criminal acts such as cyber violence and real-life hate crimes. Since Twitter is used globally by people from various backgrounds and nationalities, the platform contains tweets posted in different languages, including code-mixed language, namely Hindi-English. Due to the informal format of tweets with variations in spelling and grammar, hate speech detection is challenging, especially in code-mixed text containing a mixture of different languages. In this paper, we tackle the critical issue of hate speech on social media, with a focus on a mix of English and Hindi-English (code-mixed) text messages (tweets) on Twitter. We perform hate speech classification using the benefits of character-level embedding representations of tweets and Deep Neural Networks (DNN). We built two architectures, namely Convolutional Neural Network (CNN) and a combination of CNN and Long Short-Term Memory (LSTM) algorithms with character-level embedding as an improvement over Elouali et al. (2020)’s work. Both the models were trained using an imbalanced (original) as well as oversampled (balanced) version of the training dataset and were evaluated on the test set. Extensive experimental analysis was performed by tuning the hyperparameters of our models and evaluating their performance in terms of accuracy, efficiency (runtime) and scalability in detecting whether a tweet is hate speech or non-hate. The performance of our proposed models is compared with Elouali et al. (2020)’s model, and it is observed that our method has an improved accuracy and a significantly improved runtime and is scalable. Among our best performing models, CNN-LSTM performed slightly better than CNN with an accuracy of 88.97%