Index of papers in Proc. ACL 2014 that mention
  • end-to-end
Lee, Kenton and Artzi, Yoav and Dodge, Jesse and Zettlemoyer, Luke
Abstract
Experiments on benchmark datasets show that our approach outperforms previous state-of-the-art systems, with error reductions of 13% to 21% in end-to-end performance.
Experimental Setup
For end-to-end performance, value F1 is the primary metric.
Experimental Setup
Comparison Systems We compare our system primarily to HeidelTime (Stro'tgen and Gertz, 2013), which is state of the art in the end-to-end task.
Formal Overview
We compare to the state-of-the-art systems for end-to-end resolution (Strotgen and Gertz, 2013) and resolution given gold mentions (Bethard, 2013b), both of which do not use any machine learning techniques.
Introduction
On these benchmark datasets, we present new state-of-the-art results, with error reductions of up to 28% for the detection task and 21% for the end-to-end task.
Results
End-to-end results Figure 4 shows development and test results for TempEval-3.
Results
Precision vs. Recall Our probabilistic model of time expression resolution allows us to easily tradeoff precision and recall for end-to-end performance by varying the resolution probability threshold.
Results
We also manually categorized all resolution errors for end-to-end performance with 10-fold cross validation of the TempEval-3 Dev dataset,
end-to-end is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Li, Qi and Ji, Heng
Abstract
Experiments on Automatic Content Extraction (ACE)1 corpora demonstrate that our joint model significantly outperforms a strong pipelined baseline, which attains better performance than the best-reported end-to-end system.
Background
Most previous research on relation extraction assumed that entity mentions were given In this work we aim to address the problem of end-to-end entity mention and relation extraction from raw texts.
Conclusions and Future Work
In this paper we introduced a new architecture for more powerful end-to-end entity mention and relation extraction.
Experiments
Furthermore, we combine these two criteria to evaluate the performance of end-to-end entity mention and relation extraction.
Experiments
The human F1 score on end-to-end relation extraction is only about 70%, which indicates it is a very challenging task.
Experiments
For end-to-end entity mention and relation extraction, both the joint approach and the pipelined baseline outperform the best results reported by (Chan and Roth, 2011) under the same setting.
Introduction
The goal of end-to-end entity mention and relation extraction is to discover relational structures of entity mentions from unstructured texts.
Related Work
We extended the similar idea to our end-to-end task by incrementally predicting relations along with entity mention segments.
end-to-end is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Liu, Shujie and Yang, Nan and Li, Mu and Zhou, Ming
Abstract
In this paper, we propose a novel recursive recurrent neural network (RZNN) to model the end-to-end decoding process for statistical machine translation.
Introduction
Different from the work mentioned above, which applies DNN to components of conventional SMT framework, in this paper, we propose a novel RZNN to model the end-to-end decoding process.
Our Model
In this section, we leverage DNN to model the end-to-end SMT decoding process, using a novel recursive recurrent neural network (RZNN), which is different from the above mentioned work applying DNN to components of conventional SMT framework.
Our Model
Our R2NN is used to model the end-to-end translation process, with recurrent global information added.
Related Work
Unfortunately, the better word alignment result generated by this model, cannot bring significant performance improvement on a end-to-end SMT evaluation task.
end-to-end is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Xue, Huichao and Hwa, Rebecca
Conclusions
5 We currently do not evaluate the end-to-end system over different corpora.
Conclusions
Table 4: Extrinsic evaluation, where we plugged the two merging models into an end-to-end feedback detection system by Swanson and Yamangil.
Correction Detection
In comparison, phrase extraction systems aim to improve the end-to-end MT or paraphrasing systems.
Experimental Setup
In addition to evaluating the merging algorithms on the standalone task of correction detection, we have also plugged in the merging algorithms into an end-to-end system in which every automatically detected correction is further classified into an error type.
end-to-end is mentioned in 4 sentences in this paper.
Topics mentioned in this paper:
Bansal, Mohit and Burkett, David and de Melo, Gerard and Klein, Dan
Introduction
Our model is also the first to directly learn relational patterns as part of the process of training an end-to-end taxonomic induction system, rather than using patterns that were hand-selected or learned via pairwise classifiers on manually annotated co-occurrence patterns.
Introduction
Finally, it is the first end-to-end (i.e., non-incremental) system to include sibling (e.g., coordination) patterns at all.
Related Work
Our model also automatically learns relational patterns as a part of the taxonomic training phase, instead of relying on handpicked rules or pairwise classifiers on manually annotated co-occurrence patterns, and it is the first end-to-end (i.e., non-incremental) system to include heterogeneous relational information via sibling (e.g., coordination) patterns.
end-to-end is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Huang, Hongzhao and Cao, Yunbo and Huang, Xiaojiang and Ji, Heng and Lin, Chin-Yew
Experiments
The relatively low performance of the baseline system TagMe demonstrates that only relying on prior popularity and topical information within a single tweet is not enough for an end-to-end wikification system for the short tweets.
Experiments
However, when ,u 2 0.4, the system performance dramatically decreases, showing that prior popularity is not enough for an end-to-end wikification system.
Introduction
An end-to-end wikification system needs to solve two sub-problems: (i) concept mention detection, (ii) concept mention disambiguation.
end-to-end is mentioned in 3 sentences in this paper.
Topics mentioned in this paper:
Zhang, Jiajun and Liu, Shujie and Li, Mu and Zhou, Ming and Zong, Chengqing
Abstract
We evaluate our proposed method on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to measure semantic similarity between a source phrase and its translation candidates.
Conclusions and Future Work
Two end-to-end SMT tasks are involved to test the power of the proposed model at learning the semantic phrase embeddings.
Introduction
Accordingly, we evaluate the BRAE model on two end-to-end SMT tasks (phrase table pruning and decoding with phrasal semantic similarities) which need to check whether a translation candidate and the source phrase are in the same meaning.
end-to-end is mentioned in 3 sentences in this paper.
Topics mentioned in this paper: