A new manually annotated English-Tamil-Sinhala parallel NER corpus of 3,835 sentences per language, with benchmarks showing XLM-R outperforms monolingual and Indic models, and a case study where NER output improves English-Sinhala NMT BLEU by about 9 points.
SLING: A framework for frame semantic parsing
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abstract
We describe SLING, a framework for parsing natural language into semantic frames. SLING supports general transition-based, neural-network parsing with bidirectional LSTM input encoding and a Transition Based Recurrent Unit (TBRU) for output decoding. The parsing model is trained end-to-end using only the text tokens as input. The transition system has been designed to output frame graphs directly without any intervening symbolic representation. The SLING framework includes an efficient and scalable frame store implementation as well as a neural network JIT compiler for fast inference during parsing. SLING is implemented in C++ and it is available for download on GitHub.
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A Multi-way Parallel Named Entity Annotated Corpus for English, Tamil and Sinhala
A new manually annotated English-Tamil-Sinhala parallel NER corpus of 3,835 sentences per language, with benchmarks showing XLM-R outperforms monolingual and Indic models, and a case study where NER output improves English-Sinhala NMT BLEU by about 9 points.