Қорытынды

Even the entire Web corpus does not explic- itly answer all questions, yet inference can un- cover many implicit answers. But where do inference rules come from? This paper investigates …...

Automatically constructed Knowledge Bases (KBs) are often incomplete and there is a gen- uine need to improve their coverage. Path Ranking Algorithm (PRA) is a recently pro- posed method which …...

Inference about whether a word in one text has similar meaning to another word in the other text is an essential task in order to understand whether two texts have …...

AbstractPruning hypotheses during dynamic programming is commonly used to speed up inference in settings such as parsing. Unlike prior work, we train a pruning policy under an objective that measures …...

AbstractInference in structured prediction involves finding the best output structure for an input, subject to certain constraints. Many current approaches use sequential inference, which constructs the output in a left-to-right …...

AbstractNatural language understanding (NLU) and Natural language generation (NLG) tasks hold a strong dual relationship, where NLU aims at predicting semantic labels based on natural language utterances and NLG does …...

AbstractPolarity classification (positive, negative or neutral opinion detection) is well developed in the field of opinion mining. However, existing tools, which perform with high accuracy on short sentences and explicit …...

AbstractToday’s text classifiers inevitably suffer from unintended dataset biases, especially the document-level label bias and word-level keyword bias, which may hurt models’ generalization. Many previous studies employed data-level manipulations or …...

AbstractA mind-map is a diagram that represents the central concept and key ideas in a hierarchical way. Converting plain text into a mind-map will reveal its key semantic structure and …...

Autoregressive models have been widely used in unsupervised text style transfer. Despite their success, these models still suffer from the content preservation problem that they usu- ally ignore part of …...

AbstractWhen working with problems in natural language processing, we can find ourselves in situations where the traditional measurements of descriptive complexity are ineffective at describing the behaviour of our algorithms. …...

AbstractClassification problems having thousands or more classes naturally occur in NLP, for example language models or document classification. A softmax or one-vs-all classifier naturally handles many classes, but it is …...

AbstractState-of-the-art neural models typically encode document-query pairs using cross-attention for re-ranking. To this end, models generally utilize an encoder-only (like BERT) paradigm or an encoder-decoder (like T5) approach. These paradigms, …...

AbstractPretrained language models can be queried for factual knowledge, with potential applications in knowledge base acquisition and tasks that require inference. However, for that, we need to know how reliable …...

AbstractIn recent years, multilingual machine translation models have achieved promising performance on low-resource language pairs by sharing information between similar languages, thus enabling zero-shot translation. To overcome the “curse of …...

Abstract“Zero-shot stance detection intends to detect previously unseen targets’ stances in the testingphase. However, achieving this goal can be difficult, as it requires minimizing the domain trans-fer between different targets, …...

AbstractThe neural metrics recently received considerable attention from the research community in the automatic evaluation of machine translation. Unlike text-based metrics that have interpretable and consistent evaluation mechanisms for various …...