Loss Function

We present Minimum Bayes-Risk (MBR) de- coding for statistical machine translation. This statistical approach aims to minimize expected loss of translation errors under loss functions that measure translation performance. We …...

This paper proposes a new method for ap- proximate string search, specifically candidate generation in spelling error correction, which is a task as follows. Given a misspelled word, the system …...

Conversations provide rich opportunities for interactive, continuous learning. When some- thing goes wrong, a system can ask for clari- fication, rewording, or otherwise redirect the interaction to achieve its goals. …...

We consider the problem of NER in Arabic Wikipedia, a semisupervised domain adap- tation setting for which we have no labeled training data in the target domain. To fa- cilitate …...

AbstractEnabling machines to read and comprehend unstructured text remains an unfulfilled goal for NLP research. Recent research efforts on the “machine comprehension” task have managed to achieve close to ideal …...

AbstractSpectral models for learning weighted non-deterministic automata have nice theoretical and algorithmic properties. Despite this, it has been challenging to obtain competitive results in language modeling tasks, for two main …...

AbstractIn this paper, we develop a method for grounding medical text into a physically meaningful and interpretable space corresponding to a human atlas. We build on text embedding architectures such …...

AbstractExisting models for data-to-text tasks generate fluent but sometimes incorrect sentences e.g., “Nikkei gains” is generated when “Nikkei drops” is expected. We investigate models trained on contrastive examples i.e., incorrect …...

AbstractText autoencoders are commonly used for conditional generation tasks such as style transfer. We propose methods which are plug and play, where any pretrained autoencoder can be used, and only …...

AbstractPerformance on the Winograd Schema Challenge (WSC), a respected English commonsense reasoning benchmark, recently rocketed from chance accuracy to 89% on the SuperGLUE leaderboard, with relatively little corroborating evidence of …...

AbstractBy predicting chemical compound structures from their names, we can better comprehend chemical compounds written in text and identify the same chemical compound given different notations for database creation. Previous …...

AbstractCharge prediction aims to predict the final charge for a case according to its fact descriptionand plays an important role in legal assistance systems. With deep learning based methods prediction …...

AbstractWe propose a deep generative model that performs typography analysis and font reconstruction by learning disentangled manifolds of both font style and character shape. Our approach enables us to massively …...

AbstractEmotion recognition (ER) is an important task in Natural Language Processing (NLP), due to its high impact in real-world applications from health and well-being to author profiling, consumer analysis and …...

AbstractThis paper describes Amazon Alexa AI’s submission to the IWSLT 2022 Offline Speech Translation Task. Our system is an end-to-end speech translation model that leverages pretrained models and cross modality …...

AbstractIncreasing the number of tasks supported by a machine learning model without forgetting previously learned tasks is the goal of any lifelong learning system. In this work, we study how …...

AbstractIn this paper, we propose a method to generate personalized filled pauses (FPs) with group-wise prediction models. Compared with fluent text generation, disfluent text generation has not been widely explored. …...

AbstractMany natural language processing (NLP) tasks are naturally imbalanced, as some target categories occur much more frequently than others in the real world. In such scenarios, current NLP models tend …...

AbstractKnowledge graphs (KGs) facilitate a wide variety of applications. Despite great efforts in creation and maintenance, even the largest KGs are far from complete. Hence, KG completion (KGC) has become …...

AbstractSelective rationales and counterfactual examples have emerged as two effective, complementary classes of interpretability methods for analyzing and training NLP models. However, prior work has not explored how these methods …...

AbstractDocument-level relation extraction (DocRE) predicts relations for entity pairs that rely on long-range context-dependent reasoning in a document. As a typical multi-label classification problem, DocRE faces the challenge of effectively …...