Improving Sentence Representations via Component Focusing
نویسندگان
چکیده
منابع مشابه
Improving Visually Grounded Sentence Representations with Self-Attention
Sentence representation models trained only on language could potentially suffer from the grounding problem. Recent work has shown promising results in improving the qualities of sentence representations by jointly training them with associated image features. However, the grounding capability is limited due to distant connection between input sentences and image features by the design of the a...
متن کاملImproving Predictive State Representations via Gradient Descent
Predictive state representations (PSRs) model dynamical systems using appropriately chosen predictions about future observations as a representation of the current state. In contrast to the hidden states posited by HMMs or RNNs, PSR states are directly observable in the training data; this gives rise to a moment-matching spectral algorithm for learning PSRs that is computationally efficient and...
متن کاملImproving Source Separation via Multi-Speaker Representations
Lately there have been novel developments in deep learning towards solving the cocktail party problem. Initial results are very promising and allow for more research in the domain. One technique that has not yet been explored in the neural network approach to this task is speaker adaptation. Intuitively, information on the speakers that we are trying to separate seems fundamentally important fo...
متن کاملImproving Word Representations via Global Visual Context
Visually grounded semantics is a very important aspect in word representation, largely due to its potential to improve many NLP tasks such as information retrieval, text classification and analysis. We present a new distributed word learning framework which 1) learns word embeddings that better capture the visually grounded semantics by unifying local document context and global visual context,...
متن کاملRefining Raw Sentence Representations for Textual Entailment Recognition via Attention
In this paper we present the model used by the team Rivercorners for the 2017 RepEval shared task. First, our model separately encodes a pair of sentences into variable-length representations by using a bidirectional LSTM. Later, it creates fixed-length raw representations by means of simple aggregation functions, which are then refined using an attention mechanism. Finally it combines the refi...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: Applied Sciences
سال: 2020
ISSN: 2076-3417
DOI: 10.3390/app10030958