Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  apply cross-lingual Latent Semantic Indexing to the Bilingual Document Alignment Task at WMT16. Reduced-rank singular value decomposition of a bilingual term-document matrix derived from known English/French page pairs in the training data allows us to map monolingual documents into a joint semantic space. Two variants of cosine similarity beten the vectors that place each document into the joint semantic space are combined with a measure of string similarity beten corresponding URLs to produce 1:1 alignments of English/French b pages in a variety of domains. The system achieves a recall of ca. 88% if no in-domain data is used for building the latent semantic model, and 93% if such data is included. Analysing the system’s errors on the training data,  argue that evaluating aligner performance based on exact URL matches under-estimates their true performance and propose an alternative that is able to account for duplicates and near-duplicates in the underlying data.
['Ulrich Germann']
I am writing a research paper on a idea Bilingual Document Alignment with Latent Semantic Indexing
[{"from": "human", "value": "I am writing a research paper on a idea Bilingual Document Alignment with Latent Semantic Indexing"}, {"from": "gpt", "value": "Thats a unique idea.You should consider the following idea to enhance your idea so that it would impact uniquely:  apply cross-lingual Latent Semantic Indexing to the Bilingual Document Alignment Task at WMT16. Reduced-rank singular value decomposition of a bilingual term-document matrix derived from known English/French page pairs in the training data allows us to map monolingual documents into a joint semantic space. Two variants of cosine similarity beten the vectors that place each document into the joint semantic space are combined with a measure of string similarity beten corresponding URLs to produce 1:1 alignments of English/French b pages in a variety of domains. The system achieves a recall of ca. 88% if no in-domain data is used for building the latent semantic model, and 93% if such data is included. Analysing the system\u2019s errors on the training data,  argue that evaluating aligner performance based on exact URL matches under-estimates their true performance and propose an alternative that is able to account for duplicates and near-duplicates in the underlying data."}]