24,334 research outputs found
Investigating Language Impact in Bilingual Approaches for Computational Language Documentation
For endangered languages, data collection campaigns have to accommodate the
challenge that many of them are from oral tradition, and producing
transcriptions is costly. Therefore, it is fundamental to translate them into a
widely spoken language to ensure interpretability of the recordings. In this
paper we investigate how the choice of translation language affects the
posterior documentation work and potential automatic approaches which will work
on top of the produced bilingual corpus. For answering this question, we use
the MaSS multilingual speech corpus (Boito et al., 2020) for creating 56
bilingual pairs that we apply to the task of low-resource unsupervised word
segmentation and alignment. Our results highlight that the choice of language
for translation influences the word segmentation performance, and that
different lexicons are learned by using different aligned translations. Lastly,
this paper proposes a hybrid approach for bilingual word segmentation,
combining boundary clues extracted from a non-parametric Bayesian model
(Goldwater et al., 2009a) with the attentional word segmentation neural model
from Godard et al. (2018). Our results suggest that incorporating these clues
into the neural models' input representation increases their translation and
alignment quality, specially for challenging language pairs.Comment: Accepted to 1st Joint SLTU and CCURL Worksho
AON: Towards Arbitrarily-Oriented Text Recognition
Recognizing text from natural images is a hot research topic in computer
vision due to its various applications. Despite the enduring research of
several decades on optical character recognition (OCR), recognizing texts from
natural images is still a challenging task. This is because scene texts are
often in irregular (e.g. curved, arbitrarily-oriented or seriously distorted)
arrangements, which have not yet been well addressed in the literature.
Existing methods on text recognition mainly work with regular (horizontal and
frontal) texts and cannot be trivially generalized to handle irregular texts.
In this paper, we develop the arbitrary orientation network (AON) to directly
capture the deep features of irregular texts, which are combined into an
attention-based decoder to generate character sequence. The whole network can
be trained end-to-end by using only images and word-level annotations.
Extensive experiments on various benchmarks, including the CUTE80,
SVT-Perspective, IIIT5k, SVT and ICDAR datasets, show that the proposed
AON-based method achieves the-state-of-the-art performance in irregular
datasets, and is comparable to major existing methods in regular datasets.Comment: Accepted by CVPR201
Exploiting alignment techniques in MATREX: the DCU machine translation system for IWSLT 2008
In this paper, we give a description of the machine translation (MT) system developed at DCU that was used for our third participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2008). In this participation, we focus on various techniques for word and phrase alignment to improve system quality. Specifically, we try out our word packing and syntax-enhanced word alignment techniques for the ChineseâEnglish task and for the EnglishâChinese task for the first time. For all translation tasks except ArabicâEnglish, we exploit linguistically motivated bilingual phrase pairs extracted from parallel treebanks. We smooth our translation tables with out-of-domain word translations for the ArabicâEnglish and ChineseâEnglish tasks in order to solve the problem of the high number of out of vocabulary items. We also carried out experiments combining both in-domain and out-of-domain data to improve system performance and, finally, we deploy a majority voting procedure combining a language model based method and a translation-based method for case and punctuation restoration. We participated in all the translation
tasks and translated both the single-best ASR hypotheses and
the correct recognition results. The translation results confirm that our new word and phrase alignment techniques are often helpful in improving translation quality, and the data combination method we proposed can significantly improve system performance
Bootstrapping word alignment via word packing
We introduce a simple method to pack words for statistical word alignment. Our goal is to simplify the task of automatic word alignment by packing several consecutive words together when we believe they correspond to a single word in the opposite language. This is done using the word aligner itself, i.e. by bootstrapping on its output. We evaluate the performance of our approach on a Chinese-to-English machine translation task, and report a 12.2% relative increase in BLEU score over a state-of-the art phrase-based SMT system
MaTrEx: the DCU machine translation system for IWSLT 2007
In this paper, we give a description of the machine translation system developed at DCU that was used for our second participation in the evaluation campaign of the International Workshop on Spoken Language Translation (IWSLT 2007). In this participation, we focus on some new methods to improve system quality. Specifically, we try our word packing technique for different language pairs, we smooth our translation tables with out-of-domain word translations for the ArabicâEnglish and ChineseâEnglish tasks in order to solve the high number of out of vocabulary items, and finally we deploy a translation-based model for case and punctuation restoration
Incorporating Structured Commonsense Knowledge in Story Completion
The ability to select an appropriate story ending is the first step towards
perfect narrative comprehension. Story ending prediction requires not only the
explicit clues within the context, but also the implicit knowledge (such as
commonsense) to construct a reasonable and consistent story. However, most
previous approaches do not explicitly use background commonsense knowledge. We
present a neural story ending selection model that integrates three types of
information: narrative sequence, sentiment evolution and commonsense knowledge.
Experiments show that our model outperforms state-of-the-art approaches on a
public dataset, ROCStory Cloze Task , and the performance gain from adding the
additional commonsense knowledge is significant
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