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Most large vocabulary speech recognition systems essentially consist of a training algorithm and a recognition structure which is essentially a search for the best path through a rather large decoding …...

This paper proposes a method for incrementally un- derstanding user utterances whose semantic bound- aries are not known and responding in real time even before boundaries are determined. It is …...

This paper describes an incremental parsing approach where parameters are estimated using a variant of the perceptron algorithm. A beam-search algorithm is used during both training and decoding phases of …...

We describe an incremental parser that was trained to minimize cost over sen- tences rather than over individual parsing ac- tions. This is an attempt to use the advan- tages …...

Graph-based and transition-based approaches to dependency parsing adopt very different views of the problem, each view having its own strengths and limitations. We study both approaches under the framework of …...

Recent advances in statistical machine translation have used beam search for approximate NP-complete inference within probabilistic translation models. We present an alternative approach of sampling from the posterior distribution defined …...

We present an inexact search algorithm for the problem of predicting a two-layered dependency graph. The algorithm is based on a k-best version of the standard cubic- time search algorithm …...

A large body of prior research on coref- erence resolution recasts the problem as a two-class classification problem. How- ever, standard supervised machine learn- ing algorithms that minimize classifica- tion …...

In this paper we address the problem of solving substitution ciphers using a beam search approach. We present a concep- tually consistent and easy to implement method that improves the …...

Online learning algorithms like the percep- tron are widely used for structured predic- tion tasks. For sequential search problems, like left-to-right tagging and parsing, beam search has been successfully combined …...

Beam search is a fast and empirically effective method for translation decoding, but it lacks formal guarantees about search error. We de- velop a new decoding algorithm that combines the …...

The combinatorial space of translation derivations in phrase-based statistical ma- chine translation is given by the intersec- tion between a translation lattice and a tar- get language model. We replace …...

In this paper, we present two improve- ments to the beam search approach for solving homophonic substitution ciphers presented in Nuhn et al. (2013): An im- proved rest cost estimation …...

We present an incremental joint frame- work to simultaneously extract entity men- tions and relations using structured per- ceptron with efficient beam-search. segment-based decoder based on the ideaA of semi-Markov …...

We investigate different ways of learning structured perceptron models for coref- erence resolution when using non-local features and beam search. Our experi- mental results indicate that standard tech- niques such …...

We propose a discriminatively trained re- current neural network (RNN) that pre- dicts the actions for a fast and accurate shift-reduce dependency parser. The RNN uses its output-dependent model struc- …...

Syntactic linearization algorithms take a bag of input words and a set of optional con- straints, and construct an output sentence and its syntactic derivation simultaneously. The search problem is …...

AbstractIn the age of information exploding, multi-document summarization is attracting particular attention for the ability to help people get the main ideas in a short time. Traditional extractive methods simply …...

One major drawback of phrase-based translation is that it segments an input sen- tence into continuous phrases. To sup- port linguistically informed source discon- tinuity, in this paper we construct …...

Sequence-to-Sequence (seq2seq) modeling has rapidly become an important general- purpose NLP tool that has proven effective for many text-generation and sequence-labeling tasks. Seq2seq builds on deep neural language modeling and …...

This work explores extending attention- based neural models to include alignment information as input. We modify the at- tention component to have dependence on the current source position. The at- …...

This article describes the Aalto University entry to the English-to-Finnish news trans- lation shared task in WMT 2017. Our sys- tem is an open vocabulary neural machine translation (NMT) system, …...

AbstractTranslating in real-time, a.k.a.simultaneous translation, outputs translation words before the input sentence ends, which is a challenging problem for conventional machine translation methods. We propose a neural machine translation (NMT) …...

AbstractNeural Machine Translation (NMT) lays intensive burden on computation and memory cost. It is a challenge to deploy NMT models on the devices with limited computation and memory budgets. This …...

AbstractWe propose a novel decoding approach for neural machine translation (NMT) based on continuous optimisation. We reformulate decoding, a discrete optimization problem, into a continuous problem, such that optimization can …...

AbstractIn neural text generation such as neural machine translation, summarization, and image captioning, beam search is widely used to improve the output text quality. However, in the neural generation setting, …...

AbstractGenerative dialog models usually adopt beam search as the inference method to generate responses. However, small-width beam search only focuses on the limited current optima. This deficiency named as myopic …...

AbstractWe tackle the nested and overlapping event detection task and propose a novel search-based neural network (SBNN) structured prediction model that treats the task as a search problem on a …...

AbstractWe report our work on building linguistic resources and data-driven parsers in the grammatical relation (GR) analysis for Mandarin Chinese. Chinese, as an analytic language, encodes grammatical information in a …...

AbstractWe present set to ordered text, a natural language generation task applied to automatically generating discharge instructions from admission ICD (International Classification of Diseases) codes. This task differs from other …...

AbstractTraining models to map natural language instructions to programs, given target world supervision only, requires searching for good programs at training time. Search is commonly done using beam search in …...

AbstractDecoding for many NLP tasks requires an effective heuristic algorithm for approximating exact search because the problem of searching the full output space is often intractable, or impractical in many …...

AbstractThe standard training algorithm in neural machine translation (NMT) suffers from exposure bias, and alternative algorithms have been proposed to mitigate this. However, the practical impact of exposure bias is …...

AbstractRevealing the robustness issues of natural language processing models and improving their robustness is important to their performance under difficult situations. In this paper, we study the robustness of paraphrase …...

AbstractDecipherment of historical ciphers is a challenging problem. The language of the target plaintext might be unknown, and ciphertext can have a lot of noise. State-of-the-art decipherment methods use beam …...

AbstractBeam search is the default decoding strategy for many sequence generation tasks in NLP. The set of approximate K-best items returned by the algorithm is a useful summary of the …...

AbstractBeam search is a go-to strategy for decoding neural sequence models. The algorithm can naturally be viewed as a subset optimization problem, albeit one where the corresponding set function does …...

AbstractThe performance of natural language generation systems has improved substantially with modern neural networks. At test time they typically employ beam search to avoid locally optimal but globally suboptimal predictions. …...

AbstractGenerating goal-oriented questions in Visual Dialogue tasks is a challenging and longstanding problem. State-Of-The-Art systems are shown to generate questions that, although grammatically correct, often lack an effective strategy and …...

AbstractBeam search is the go-to method for decoding auto-regressive machine translation models. While it yields consistent improvements in terms of BLEU, it is only concerned with finding outputs with high …...

AbstractConditional text generation often requires lexical constraints, i.e., which words should or shouldn’t be included in the output text. While the dominant recipe for conditional text generation has been large-scale …...

We release our synthetic parallel paraphrase corpus across 17 languages: Arabic, Catalan, Czech, German, English, Spanish, Estonian, French, Hindi, Indonesian, Italian, Dutch, Ro- manian, Russian, Swedish, Vietnamese, and Chinese. Our …...

AbstractNeural machine translation (NMT) usually employs beam search to expand the searching spaceand obtain more translation candidates. However the increase of the beam size often suffersfrom plenty of short translations …...

AbstractWe study the problem of generating counterfactual text for a classifier as a means for understanding and debugging classification. Given a textual input and a classification model, we aim to …...

AbstractWe describe our submission to the IWSLT 2021 shared task on simultaneous text-to-text English-German translation. Our system is based on the re-translation approach where the agent re-translates the whole source …...

AbstractHuman-like biases and undesired social stereotypes exist in large pretrained language models. Given the wide adoption of these models in real-world applications, mitigating such biases has become an emerging and …...

AbstractThis paper presents novel methods to automatically convert posts and their comments from discussion forums such as Reddit into multi-turn dialogues. Our methods are generalizable to any forums; thus, they …...

AbstractPrompting, which casts downstream applications as language modeling tasks, has shown to be sample efficient compared to standard fine-tuning with pre-trained models. However, one pitfall of prompting is the need …...

AbstractNeural autoregressive sequence models smear the probability among many possible sequences including degenerate ones, such as empty or repetitive sequences. In this work, we tackle one specific case where the …...

AbstractBack-translation is a well established approach to improve the performance of Neural Machine Translation (NMT) systems when large monolingual corpora of the target language and domain are available. Recently, diverse …...

AbstractSemantic parsing (SP) is a core component of modern virtual assistants like Google Assistant and Amazon Alexa. While sequence-to-sequence based auto-regressive (AR) approaches are common for conversational SP, recent studies …...

AbstractBack translation (BT) is one of the most significant technologies in NMT research fields. Existing attempts on BT share a common characteristic: they employ either beam search or random sampling …...

AbstractAspect-based sentiment analysis (ABSA) tasks aim to extract sentiment tuples from a sentence. Recent generative methods such as Seq2Seq models have achieved good performance by formulating the output as a …...

AbstractModern natural language generation paradigms require a decoding strategy to obtain quality sequences out of the model. Beam search yields high-quality but low diversity outputs; stochastic approaches suffer from high …...

AbstractDespite the recent advances in dialogue state tracking (DST), the joint goal accuracy (JGA) of the existing methods on MultiWOZ 2.1 still remains merely 60%. In our preliminary error analysis, …...

AbstractBeam search is the most popular decoding method for Neural Machine Translation (NMT) and is still a strong baseline compared with the newly proposed sampling-based methods. To better understand beam …...

AbstractTwo-step approaches, in which summary candidates are generated-then-reranked to return a single summary, can improve ROUGE scores over the standard single-step approach. Yet, standard decoding methods (i.e., beam search, nucleus …...

AbstractFeature attribution methods are popular for explaining neural network predictions, and they are often evaluated on metrics such as comprehensiveness and sufficiency. In this paper, we highlight an intriguing property …...