otherentity ID: parameters

Related Papers

SRI International Results February 1992 ATIS Benchmark Test

Authors: nan

Publication: TIPSTER (1992)

DOI: nan

We describe the results that SRI International achieved on the February 1992 ATIS Speech and Natural Language Sys- tem Test. The basic architecture of the system is described, including a …...

13017

A Distributional Analysis of a Lexicalized Statistical Parsing Model

Authors: nan

Publication: TIPSTER (2004)

DOI: nan

This paper presents some of the first data visualiza- tions and analysis of distributions for a lexicalized statistical parsing model, in order to better under- stand their nature. In the …...

44953

Exploiting Web-Derived Selectional Preference to Improve Statistical Dependency Parsing

Authors: nan

Publication: TIPSTER (2011)

DOI: nan

In this paper, we present a novel approach which incorporates the web-derived selec- tional preferences to improve statistical de- pendency parsing. Conventional selectional preference learning methods have usually fo- cused …...

36254

Discriminative Training of 150 Million Translation Parameters and Its Application to Pruning

Authors: nan

Publication: TIPSTER (2013)

DOI: nan

Until recently, the application of discrimina- tive training to log linear-based statistical ma- chine translation has been limited to tuning the weights of a limited number of features or training …...

23710

Neural Scoring Function for MST Parser

Authors: nan

Publication: TIPSTER (2016)

DOI: nan

AbstractContinuous word representations appeared to be a useful feature in many natural language processing tasks. Using fixed-dimension pre-trained word embeddings allows avoiding sparse bag-of-words representation and to train models with …...

16693

Simple and Effective Text Matching with Richer Alignment Features

Authors: nan

Publication: TIPSTER (2019)

DOI: nan

AbstractIn this paper, we present a fast and strong neural approach for general purpose text matching applications. We explore what is sufficient to build a fast and well-performed text matching …...

39499

Investigating Catastrophic Forgetting During Continual Training for Neural Machine Translation

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractNeural machine translation (NMT) models usually suffer from catastrophic forgetting during continual training where the models tend to gradually forget previously learned knowledge and swing to fit the newly added …...

3984

On the Sparsity of Neural Machine Translation Models

Authors: nan

Publication: TIPSTER (2020)

DOI: nan

AbstractModern neural machine translation (NMT) models employ a large number of parameters, which leads to serious over-parameterization and typically causes the underutilization of computational resources. In response to this problem, …...

31093

One Semantic Parser to Parse Them All: Sequence to Sequence Multi-Task Learning on Semantic Parsing Datasets

Authors: nan

Publication: TIPSTER (2021)

DOI: nan

AbstractSemantic parsers map natural language utterances to meaning representations. The lack of a single standard for meaning representations led to the creation of a plethora of semantic parsing datasets. To …...

25259

ATTEMPT: Parameter-Efficient Multi-task Tuning via Attentional Mixtures of Soft Prompts

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractThis work introduces a new multi-task, parameter-efficient language model (LM) tuning method that learns to transfer knowledge across different tasks via a mixture of soft prompts—small prefix embedding vectors pre-trained …...

30558

COST-EFF: Collaborative Optimization of Spatial and Temporal Efficiency with Slenderized Multi-exit Language Models

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractTransformer-based pre-trained language models (PLMs) mostly suffer from excessive overhead despite their advanced capacity. For resource-constrained devices, there is an urgent need for a spatially and temporally efficient model which …...

31455

HashFormers: Towards Vocabulary-independent Pre-trained Transformers

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractTransformer-based pre-trained language models are vocabulary-dependent, mapping by default each token to its corresponding embedding. This one-to-one mapping results into embedding matrices that occupy a lot of memory (i.e. millions …...

30402

Towards Efficient Dialogue Pre-training with Transferable and Interpretable Latent Structure

Authors: nan

Publication: TIPSTER (2022)

DOI: nan

AbstractWith the availability of massive general-domain dialogue data, pre-trained dialogue generation appears to be super appealing to transfer knowledge from the general domain to downstream applications. In most existing work, …...

30114