Index of papers in Proc. ACL 2014 that mention
  • cross-lingual
Lo, Chi-kiu and Beloucif, Meriem and Saers, Markus and Wu, Dekai
Abstract
We introduce XMEANT—a new cross-lingual version of the semantic frame based MT evaluation metric MEAN T—which can correlate even more closely with human adequacy judgments than monolingual MEANT and eliminates the need for expensive human references.
Abstract
However, to go beyond tuning weights in the loglinear SMT model, a cross-lingual objective function that can deeply integrate semantic frame criteria into the MT training pipeline is needed.
Abstract
We show that cross-lingual XMEANT outperforms monolingual MEANT by (l) replacing the monolingual context vector model in MEANT with simple translation probabilities, and (2) incorporating bracketing ITG constraints.
Introduction
We show that XMEANT, a new cross-lingual version of MEANT (Lo et al., 2012), correlates with human judgment even more closely than MEANT for evaluating MT adequacy via semantic frames, despite discarding the need for expensive human reference translations.
Introduction
Our results suggest that MT translation adequacy is more accurately evaluated via the cross-lingual semantic frame similarities of the input and the MT output which may obviate the need for expensive human reference translations.
Introduction
In order to continue driving MT towards better translation adequacy by deeply integrating semantic frame criteria into the MT training pipeline, it is necessary to have a cross-lingual semantic objective function that assesses the semantic frame similarities of input and output sentences.
Related Work
Evaluating cross-lingual MT quality is similar to the work of MT quality estimation (QE).
Related Work
Figure 3: Cross-lingual XMEANT algorithm.
XMEANT: a cross-lingual MEANT
But whereas MEANT measures lexical similarity using a monolingual context vector model, XMEANT instead substitutes simple cross-lingual lexical translation probabilities.
XMEANT: a cross-lingual MEANT
To aggregate individual lexical translation probabilities into phrasal similarities between cross-lingual semantic role fillers, we compared two natural approaches to generalizing MEANT’s method of comparing semantic parses, as described below.
cross-lingual is mentioned in 18 sentences in this paper.
Topics mentioned in this paper:
Tsvetkov, Yulia and Boytsov, Leonid and Gershman, Anatole and Nyberg, Eric and Dyer, Chris
Conclusion
Second, cross-lingual model transfer can be improved with more careful cross-lingual feature projection.
Experiments
5.3 Cross-lingual experiments
Experiments
Figure 2: Cross-lingual experiment: ROC curves for classifiers trained on the English data using a combination of all features, and applied to SVO and AN metaphoric and literal relations in four test languages: English, Russian, Spanish, and Farsi.
Experiments
Table 5: Cross-lingual experiment: f -scores for classifiers trained on the English data using a combination of all features, and applied, with optimal thresholds, to SVO and AN metaphoric and literal relations in four test languages: English, Russian, Spanish, and Farsi.
Methodology
To test this hypothesis, we use a cross-lingual model transfer approach: we use bilingual dictionaries to project words from other syntactic constructions found in other languages into English and then apply the English model on the derived conceptual representations.
Methodology
In addition, this coarse semantic categorization is preserved in translation (Schneider et al., 2013), which makes supersense features suitable for cross-lingual approaches such as ours.
Methodology
(2013) reveal an interesting cross-lingual property of distributed word representations: there is a strong similarity between the vector spaces across languages that can be easily captured by linear mapping.
Model and Feature Extraction
In this section we describe a classification model, and provide details on mono- and cross-lingual implementation of features.
Model and Feature Extraction
3.3 Cross-lingual feature projection
Related Work
(2013) propose a cross-lingual detection method that uses only English lexical resources and a dependency parser.
Related Work
Studies showed that cross-lingual evidence allows one to achieve a state-of-the-art performance in the WSD task, yet, most cross-lingual WSD methods employ parallel corpora (Navigli, 2009).
cross-lingual is mentioned in 11 sentences in this paper.
Topics mentioned in this paper:
Hermann, Karl Moritz and Blunsom, Phil
Abstract
We evaluate these models on two cross-lingual document classification tasks, outperforming the prior state of the art.
Conclusion
Coupled with very simple composition functions, vectors learned with this method outperform the state of the art on the task of cross-lingual document classification.
Corpora
The Europarl corpus v71 (Koehn, 2005) was used during initial development and testing of our approach, as well as to learn the representations used for the Cross-Lingual Document Classification task described in §5.2.
Experiments
First, we replicate the cross-lingual document classification task of Klementiev et al.
Experiments
We evaluate our models on the cross-lingual document classification (CLDC, henceforth) task first described in Klementiev et al.
Experiments
Cross-lingual compositional representations (ADD, BI and their multilingual extensions), I—Matrix (Klementiev et al., 2012) translated (MT) and glossed (Glossed) word baselines, and the majority class baseline.
Introduction
First, we show that for cross-lingual document classification on the Reuters RCVIRCV2 corpora (Lewis et al., 2004), we outperform the prior state of the art (Klementiev et al., 2012).
Related Work
(2013) train a cross-lingual encoder, where an autoencoder is used to recreate words in two languages in parallel.
cross-lingual is mentioned in 8 sentences in this paper.
Topics mentioned in this paper:
Schamoni, Shigehiko and Hieber, Felix and Sokolov, Artem and Riezler, Stefan
Abstract
In large-scale experiments for patent prior art search and cross-lingual retrieval in Wikipedia, our approach yields considerable improvements over leaming-to-rank with either only dense or only sparse features, and over very competitive baselines that combine state-of-the-art machine translation and retrieval.
Conclusion
Special domains such as patents or Wikipedia offer the possibility to extract cross-lingual relevance data from citation and link graphs.
Conclusion
These data can be used to directly optimizing cross-lingual ranking models.
Introduction
(2010) show that for the domain of Wikipedia, learning a sparse matrix of word associations between the query and document vocabularies from relevance rankings is useful in monolingual and cross-lingual retrieval.
Introduction
(2013) apply the idea of learning a sparse matrix of bilingual phrase associations from relevance rankings to cross-lingual retrieval in the patent domain.
Related Work
Both approaches work in a cross-lingual setting, the former on Wikipedia data, the latter on patents.
Related Work
features, and by evaluating both approaches on cross-lingual retrieval for patents and Wikipedia.
cross-lingual is mentioned in 7 sentences in this paper.
Topics mentioned in this paper:
Dinu, Georgiana and Baroni, Marco
Abstract
We test this in a monolingual scenario (paraphrase generation) as well as in a cross-lingual setting (translation by synthesizing adjective-noun phrase vectors in English and generating the equivalent expressions in Italian).
Evaluation setting
The Italian language vectors for the cross-lingual experiments of Section 6 were trained on 1.6 billion tokens from itWaC.5 A word token is a word-form + POS-tag string.
Noun phrase translation
This section describes preliminary experiments performed in a cross-lingual setting on the task of composing English AN phrases and generating Italian translations.
Noun phrase translation
Creation of cross-lingual vector spaces A common semantic space is required in order to map words and phrases across languages.
Noun phrase translation
Cross-lingual decomposition training Training proceeds as in the monolingual case, this time concatenating the training data sets and estimating a single (de)-composition function for the two languages in the shared semantic space.
cross-lingual is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Ma, Xuezhe and Xia, Fei
Abstract
We train probabilistic parsing models for resource-poor languages by transferring cross-lingual knowledge from resource-rich language with entropy regularization.
Data and Tools
However, previous studies (McDonald et al., 2011; McDonald et al., 2013) have demonstrated that a homogeneous representation is critical for multilingual language technologies that require consistent cross-lingual analysis for downstream components, and the heterogenous representations used in CoNLL shared-tasks treebanks weaken any conclusion that can be drawn.
Experiments
By using IGT Data, not only can we obtain more accurate word alignments, but also extract useful cross-lingual information for the resource-poor language.
Introduction
We extend this learning framework so that it can be used to transfer cross-lingual knowledge between different languages.
Our Approach
For the purpose of transferring cross-lingual information from the English parser via parallel text, we explore the model training method proposed by Smith and Eisner (2007), which presented a generalization of K function (Abney, 2004), and related it to another semi-supervised learning technique, entropy regularization (Jiao et al., 2006; Mann and McCallum, 2007).
cross-lingual is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Qian, Longhua and Hui, Haotian and Hu, Ya'nan and Zhou, Guodong and Zhu, Qiaoming
Abstract
(2010) propose a cross-lingual annotation projection approach which uses parallel corpora to acquire a relation detector on the target language.
Abstract
SL—CR (Supervised Learning with cross-lingual labeled instances): in addition to monolingual labeled instances (SL—MO), the training data for supervised learning contain labeled instances translated from the other language.
Abstract
AL-CR (Active Learning with cross-lingual instances): both the manually labeled instances and their translated ones are added to the respective training data.
cross-lingual is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
van Gompel, Maarten and van den Bosch, Antal
Abstract
We study the feasibility of exploiting cross-lingual context to obtain high-quality translation suggestions that improve over statistical language modelling and word-sense disambiguation baselines.
Experiments & Results
The latter study on cross-lingual WSD finds a positive impact when conducting feature selection per classifier.
Introduction
The cross-lingual context in our research question may at first seem artificial, but its design explicitly aims at applications related to computer-aided language learning (Laghos and Panayiotis, 2005; Levy, 1997) and computer-aided translation (Barrachina et al., 2009).
Introduction
The main research question in this research is how to disambiguate an L1 word or phrase to its L2 translation based on an L2 context, and whether such cross-lingual contextual approaches provide added value compared to baseline models that are not context informed or compared to standard language models.
System
The choice for this algorithm is motivated by the fact that it handles multiple classes with ease, but first and foremost because it has been successfully employed for word sense disambiguation in other studies (Hoste et al., 2002; Decadt et al., 2004), in particular in cross-lingual word sense disambiguation, a task closely resembling our current task (van Gompel and van den Bosch, 2013).
cross-lingual is mentioned in 5 sentences in this paper.
Topics mentioned in this paper:
Zhang, Jiajun and Liu, Shujie and Li, Mu and Zhou, Ming and Zong, Chengqing
Conclusions and Future Work
3) we will apply the BRAE model in other monolingual and cross-lingual tasks.
Discussions
Besides SMT, the semantic phrase embeddings can be used in other cross-lingual tasks, such as cross-lingual question answering, since the semantic similarity between phrases in different languages can be calculated accurately.
Discussions
In addition to the cross-lingual applications, we believe the BRAE model can be applied in many
Introduction
Besides SMT, the semantic phrase embeddings can be used in other cross-lingual tasks (e.g.
Introduction
cross-lingual question answering) and monolingual applications such as textual entailment, question answering and paraphrase detection.
cross-lingual is mentioned in 5 sentences in this paper.
Topics mentioned in this paper: