January 5, 2021 ABSTRACTIVE TEXT SUMMARIZATION, LANGUAGE MODELLING, machine translation, Neural MT, NMT

Subformer: Exploring Weight Sharing for Parameter Efficiency in Generative Transformers

The advent of the Transformer can arguably be described as a driving force behind many of the recent advances in natural language processing. However, despite their sizeable performance improvements, as recently shown, the model is severely over-parameterized, being parameter inefficient and computationally expensive to train… Inspired by the success of parameter-sharing in pretrained deep contextualized word representation encoders, we explore parameter-sharing methods in Transformers, with a specific focus on encoder-decoder models for sequence-to-sequence tasks such as neural machine translation. We […]

January 2, 2021 LANGUAGE MODELLING, machine translation, Neural MT, NMT, TEXT GENERATION

Directed Beam Search: Plug-and-Play Lexically Constrained Language Generation

Large pre-trained language models are capable of generating realistic text. However, controlling these models so that the generated text satisfies lexical constraints, i.e., contains specific words, is a challenging problem… Given that state-of-the-art language models are too large to be trained from scratch in a manageable time, it is desirable to control these models without re-training them. Methods capable of doing this are called plug-and-play. Recent plug-and-play methods have been successful in constraining small bidirectional language models as well as […]

December 6, 2020 LANGUAGE MODELLING, machine translation, NATURAL LANGUAGE INFERENCE, Neural MT, NMT

Data-Informed Global Sparseness in Attention Mechanisms for Deep Neural Networks

The attention mechanism is a key component of the neural revolution in Natural Language Processing (NLP). As the size of attention-based models has been scaling with the available computational resources, a number of pruning techniques have been developed to detect and to exploit sparseness in such models in order to make them more efficient… The majority of such efforts have focused on looking for attention patterns and then hard-coding them to achieve sparseness, or pruning the weights of the attention […]

September 29, 2020 LANGUAGE MODELLING, Machine Learning

Grounded Compositional Outputs for Adaptive Language Modeling

Language models have emerged as a central component across NLP, and a great deal of progress depends on the ability to cheaply adapt them (e.g., through finetuning) to new domains and tasks. A language model’s emph{vocabulary}—typically selected before training and permanently fixed later—affects its size and is part of what makes it resistant to such adaptation… Prior work has used compositional input embeddings based on surface forms to ameliorate this issue. In this work, we go one step beyond and […]

September 20, 2020 LANGUAGE MODELLING, Machine Learning Leave a comment

DSC IIT-ISM at SemEval-2020 Task 6: Boosting BERT with Dependencies for Definition Extraction

We explore the performance of Bidirectional Encoder Representations from Transformers (BERT) at definition extraction. We further propose a joint model of BERT and Text Level Graph Convolutional Network so as to incorporate dependencies into the model… Our proposed model produces better results than BERT and achieves comparable results to BERT with fine tuned language model in DeftEval (Task 6 of SemEval 2020), a shared task of classifying whether a sentence contains a definition or not (Subtask 1). (read more) PDF […]