401 research outputs found
Incorporating source-language paraphrases into phrase-based SMT with confusion networks
To increase the model coverage, sourcelanguage paraphrases have been utilized to boost SMT system performance. Previous
work showed that word lattices constructed from paraphrases are able to reduce out-ofvocabulary words and to express inputs in different ways for better translation quality.
However, such a word-lattice-based method suffers from two problems: 1) path duplications in word lattices decrease the capacities for potential paraphrases; 2) lattice decoding in SMT dramatically increases the search space and results in poor time efficiency. Therefore, in this paper, we adopt word confusion networks as the input structure to carry source-language paraphrase information. Similar to previous work, we use word lattices to build word confusion networks for merging of duplicated paths and faster decoding. Experiments are carried out on small-, medium- and large-scale English–
Chinese translation tasks, and we show that compared with the word-lattice-based method, the decoding time on three tasks is reduced significantly (up to 79%) while comparable
translation quality is obtained on the largescale task
Facilitating translation using source language paraphrase lattices
For resource-limited language pairs, coverage of the test set by the parallel corpus is an important factor that affects translation quality in two respects: 1) out of vocabulary words; 2) the same information in an input
sentence can be expressed in different ways, while current phrase-based SMT systems cannot automatically select an alternative way to transfer the same information. Therefore,
given limited data, in order to facilitate translation
from the input side, this paper proposes a novel method to reduce the translation difficulty using source-side lattice-based paraphrases. We utilise the original phrases from the input sentence and the corresponding paraphrases to build a lattice with estimated weights for each edge to improve translation quality. Compared to the baseline system, our method achieves relative improvements of 7.07%, 6.78% and 3.63% in terms of BLEU score on small, medium and largescale
English-to-Chinese translation tasks respectively. The results show that the proposed method is effective not only for resourcelimited language pairs, but also for resource sufficient pairs to some extent
Improved phrase-based SMT with syntactic reordering patterns learned from lattice scoring
In this paper, we present a novel approach to incorporate source-side syntactic reordering patterns into phrase-based SMT. The main contribution of this work is to use the lattice scoring approach to exploit and utilize reordering
information that is favoured by the baseline PBSMT system. By referring to the parse trees of the training corpus, we represent the observed reorderings with source-side
syntactic patterns. The extracted patterns are then used to convert the parsed inputs into word lattices, which contain both the original source sentences and their potential reorderings. Weights of the word lattices are estimated from the observations of the syntactic reordering patterns in the training corpus. Finally, the PBSMT system is tuned
and tested on the generated word lattices to show the benefits of adding potential sourceside reorderings in the inputs. We confirmed the effectiveness of our proposed method on a medium-sized corpus for Chinese-English
machine translation task. Our method outperformed the baseline system by 1.67% relative on a randomly selected testset and 8.56% relative on the NIST 2008 testset in terms of BLEU score
CCG contextual labels in hierarchical phrase-based SMT
In this paper, we present a method to employ target-side syntactic contextual information in a Hierarchical Phrase-Based system. Our method uses Combinatory Categorial Grammar (CCG) to annotate training data with labels that represent the left and right syntactic context of target-side phrases. These labels are then used to assign labels to nonterminals in hierarchical rules. CCG-based contextual labels help
to produce more grammatical translations by forcing phrases which replace nonterminals during translations to comply with the contextual constraints imposed by the labels. We present experiments which examine the performance of CCG contextual labels on Chinese–English and Arabic–English translation in the news and speech expressions domains using different data sizes and CCG-labeling settings. Our experiments show that our CCG contextual labels-based system achieved a 2.42% relative BLEU improvement over a PhraseBased baseline on Arabic–English translation and a 1% relative BLEU improvement over a Hierarchical Phrase-Based system baseline on Chinese–English translation
Source-side syntactic reordering patterns with functional words for improved phrase-based SMT
Inspired by previous source-side syntactic reordering methods for SMT, this paper focuses on using automatically learned syntactic reordering patterns with functional words which indicate structural reorderings between the source and target language. This approach takes advantage of phrase alignments and source-side parse trees for pattern extraction, and then filters out those patterns without functional words. Word lattices transformed by the generated patterns are fed into PBSMT systems to incorporate potential reorderings from the inputs. Experiments are carried out on a medium-sized corpus for a Chinese–English SMT task. The proposed method outperforms the baseline system by 1.38% relative on a randomly selected testset and 10.45% relative on the NIST 2008 testset in terms of BLEU score. Furthermore, a system with just 61.88% of the patterns filtered by functional words obtains a comparable performance with the unfiltered one on the randomly selected testset, and achieves 1.74% relative improvements on the NIST 2008 testset
Lattice score based data cleaning for phrase-based statistical machine translation
Statistical machine translation relies heavily
on parallel corpora to train its models
for translation tasks. While more and
more bilingual corpora are readily available,
the quality of the sentence pairs
should be taken into consideration. This
paper presents a novel lattice score-based
data cleaning method to select proper sentence
pairs from the ones extracted from a
bilingual corpus by the sentence alignment
methods. The proposed method is carried
out as follows: firstly, an initial phrasebased
model is trained on the full sentencealigned
corpus; then for each of the sentence
pairs in the corpus, word alignments
are used to create anchor pairs and sourceside
lattices; thirdly, based on the translation
model, target-side phrase networks
are expanded on the lattices and Viterbi
searching is used to find approximated decoding
results; finally, BLEU score thresholds
are used to filter out the low-score
sentence pairs for the data cleaning purpose.
Our experiments on the FBIS corpus
showed improvements of BLEU score
from 23.78 to 24.02 in Chinese-English
The Derived Ring of Differential Operators
By reading a standard formula for the ring of Grothendieck differential
operators in a derived way, we construct a derived (sheaf of) ring of
Grothendieck differential operators for Noetherian schemes separated and
finite-type over a base , when the map is finite tor-amplitude.
Using this ring of differential operators, we (re-)develop the theory of
-modules from scratch and show an equivalence of categories between
-modules using our definition and crystals over the infinitesimal site.Comment: 46 page
Grothendieck Duality via Diagonally Supported Sheaves
Following a formula found in the paper of Avramov, Iyengar, Lipman, and Nayak
(2010) and ideas of Neeman and Khusyairi, we indicate that Grothendieck duality
for finite tor-amplitude maps can be developed from scratch via the formula
. Our strategy centers on the subcategory
of quasicoherent sheaves on supported on the diagonal. By exclusively using this subcategory
instead of the full category we give systematic
categorical proofs of results in Grothendieck duality and reprove many formulas
found in Neeman (2018). We also relate some results in Grothendieck duality
with properties of the sheaf of (derived) Grothendieck differential operators.Comment: 27 page
Financial sentiment analysis using FinBERT with application in predicting stock movement
We apply sentiment analysis in financial context using FinBERT, and build a
deep neural network model based on LSTM to predict the movement of financial
market movement. We apply this model on stock news dataset, and compare its
effectiveness to BERT, LSTM and classical ARIMA model. We find that sentiment
is an effective factor in predicting market movement. We also propose several
method to improve the model.Comment: CS224U projec
Lift & Project Systems Performing on the Partial-Vertex-Cover Polytope
We study integrality gap (IG) lower bounds on strong LP and SDP relaxations
derived by the Sherali-Adams (SA), Lovasz-Schrijver-SDP (LS+), and
Sherali-Adams-SDP (SA+) lift-and-project (L&P) systems for the
t-Partial-Vertex-Cover (t-PVC) problem, a variation of the classic Vertex-Cover
problem in which only t edges need to be covered. t-PVC admits a
2-approximation using various algorithmic techniques, all relying on a natural
LP relaxation. Starting from this LP relaxation, our main results assert that
for every epsilon > 0, level-Theta(n) LPs or SDPs derived by all known L&P
systems that have been used for positive algorithmic results (but the Lasserre
hierarchy) have IGs at least (1-epsilon)n/t, where n is the number of vertices
of the input graph. Our lower bounds are nearly tight.
Our results show that restricted yet powerful models of computation derived
by many L&P systems fail to witness c-approximate solutions to t-PVC for any
constant c, and for t = O(n). This is one of the very few known examples of an
intractable combinatorial optimization problem for which LP-based algorithms
induce a constant approximation ratio, still lift-and-project LP and SDP
tightenings of the same LP have unbounded IGs.
We also show that the SDP that has given the best algorithm known for t-PVC
has integrality gap n/t on instances that can be solved by the level-1 LP
relaxation derived by the LS system. This constitutes another rare phenomenon
where (even in specific instances) a static LP outperforms an SDP that has been
used for the best approximation guarantee for the problem at hand. Finally, one
of our main contributions is that we make explicit of a new and simple
methodology of constructing solutions to LP relaxations that almost trivially
satisfy constraints derived by all SDP L&P systems known to be useful for
algorithmic positive results (except the La system).Comment: 26 page
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