Best-First Beam Search

07/08/2020
by   Clara Meister, et al.
0

Decoding for many NLP tasks requires a heuristic algorithm for approximating exact search since the full search space is often intractable if not simply too large to traverse efficiently. The default algorithm for this job is beam search–a pruned version of breadth-first search–which in practice, returns better results than exact inference due to beneficial search bias. In this work, we show that standard beam search is a computationally inefficient choice for many decoding tasks; specifically, when the scoring function is a monotonic function in sequence length, other search algorithms can be used to reduce the number of calls to the scoring function (e.g., a neural network), which is often the bottleneck computation. We propose best-first beam search, an algorithm that provably returns the same set of results as standard beam search, albeit in the minimum number of scoring function calls to guarantee optimality (modulo beam size). We show that best-first beam search can be used with length normalization and mutual information decoding, among other rescoring functions. Lastly, we propose a memory-reduced variant of best-first beam search, which has a similar search bias in terms of downstream performance, but runs in a fraction of the time.

READ FULL TEXT
research
09/17/2019

BSDAR: Beam Search Decoding with Attention Reward in Neural Keyphrase Generation

This study mainly investigates two decoding problems in neural keyphrase...
research
04/06/2022

Beam Search: Faster and Monotonic

Beam search is a popular satisficing approach to heuristic search proble...
research
03/17/2020

Learning to Accelerate Decomposition for Multi-Directional 3D Printing

Multi-directional 3D printing has the capability of decreasing or elimin...
research
04/23/2020

gBeam-ACO: a greedy and faster variant of Beam-ACO

Beam-ACO, a modification of the traditional Ant Colony Optimization (ACO...
research
09/22/2021

Conditional Poisson Stochastic Beam Search

Beam search is the default decoding strategy for many sequence generatio...
research
02/23/2022

Enabling arbitrary translation objectives with Adaptive Tree Search

We introduce an adaptive tree search algorithm, that can find high-scori...
research
04/01/2022

Uncertainty Determines the Adequacy of the Mode and the Tractability of Decoding in Sequence-to-Sequence Models

In many natural language processing (NLP) tasks the same input (e.g. sou...

Please sign up or login with your details

Forgot password? Click here to reset