Neural machine translation (NMT) becomes a new state-of-the-art and achieves promising translation results using a simple encoder-decoder neural network. This neural network is trained once on the parallel corpus and the fixed network is used to translate all the test sentences. We argue that the general fixed network cannot best fit the specific test sentences. In this paper, we propose the dynamic NMT which learns a general network as usual, and then fine-tunes the network for each test sentence. The fine-tune work is done on a small set of the bilingual training data that is obtained through similarity search according to the test sentence. Extensive experiments demonstrate that this method can significantly improve the translation performance, especially when highly similar sentences are available.
Finding similar sentences with inverted index is fast enough in our experiments. If the training data is much larger than ours, locality sensitive hash such as MinHash @cite_13 may be a better choice.
@cite_13
77954401 2017645867 2171572695 2136070674
We present experimental analysis of approximate search algorithms that involve indexing of deletion neighborhoods. These methods require huge indices whose sizes grow exponentially with respect to the maximum allowable number of errors k. Despite extraordinary space requirements, the super-linear indices are of great interest, because they provide some of the shortest retrieval times. A straightforward implementation that creates a hash index directly over residual strings (obtained by deletions from dictionary words) is not space efficient. Rather than memorizing complete residual strings, we record only deleted characters and their respective positions. These data are indexed using a perfect hash function computed for a set of residual dictionary strings [2]. We carry out an experimental evaluation of this approach against several well-known benchmarks (including FastSS, which stores residual strings directly [3]). Experiments show that our implementation has a comparable or superior performance to that of the fastest benchmarks. At the same time, our implementation requires 4-8 times less space as compared to FastSS. Abstract.The efficient retrieval of data items on set-valued attributes is an important research topic that has attracted little attention so far. We studied and modified four index structures (sequential signature files, signature trees, extendible signature hashing, and inverted files) for a fast retrieval of sets with low cardinality. We compared the index structures by implementing them and subjecting them to extensive experiments, investigating the influence of query set size, database size, domain size, and data distribution (synthetic and real). The results of the experiments clearly indicate that inverted files exhibit the best overall behavior of all tested index structures. A new data structure for efficient similarity search in very large datasets of high-dimensional vectors is introduced. This structure called the inverted multi-index generalizes the inverted index idea by replacing the standard quantization within inverted indices with product quantization. For very similar retrieval complexity and pre-processing time, inverted multi-indices achieve a much denser subdivision of the search space compared to inverted indices, while retaining their memory efficiency. Our experiments with large datasets of SIFT and GIST vectors demonstrate that because of the denser subdivision, inverted multi-indices are able to return much shorter candidate lists with higher recall. Augmented with a suitable reranking procedure, multi-indices were able to significantly improve the speed of approximate nearest neighbor search on the dataset of 1 billion SIFT vectors compared to the best previously published systems, while achieving better recall and incurring only few percent of memory overhead. Web search engines depend on the full-text inverted index data structure. Because the query processing performance is so dependent on the size of the inverted index, a plethora of research has focused on fast end effective techniques for compressing this structure. Recently, several authors have proposed techniques for improving index compression by optimizing the assignment of document identifiers to the documents in the collection, leading to significant reduction in overall index size. In this paper, we propose improved techniques for document identifier assignment. Previous work includes simple and fast heuristics such as sorting by URL, as well as more involved approaches based on the Traveling Salesman Problem or on graph partitioning. These techniques achieve good compression but do not scale to larger document collections. We propose a new framework based on performing a Traveling Salesman computation on a reduced sparse graph obtained through Locality Sensitive Hashing. This technique achieves improved compression while scaling to tens of millions of documents. Based on this framework, we describe a number of new algorithms, and perform a detailed evaluation on three large data sets showing improvements in index size.