Transformer-based pre-trained models, such as BERT, have achieved remarkable results on machine reading comprehension.
However, due to the constraint of encoding length (e.g., 512 WordPiece tokens), a long document is usually split into multiple chunks that are independently read.
It results in the reading field being limited to individual chunks without information collaboration for long document machine reading comprehension.
To address this problem, we propose RoR, a read-over-read method, which expands the reading field from chunk to document.
Specifically, RoR includes a chunk reader and a document reader.
The former first predicts a set of regional answers for each chunk, which are then compacted into a highly-condensed version of the original document, guaranteeing to be encoded once.
The latter further predicts the global answers from this condensed document.
Eventually, a voting strategy is utilized to aggregate and rerank the regional and global answers for final prediction.
Extensive experiments on two benchmarks QuAC and TriviaQA demonstrate the effectiveness of RoR for long document reading. 
Notably, RoR ranks 1st place on the QuAC leaderboard (https://quac.ai/) at the time of submission (May 17th, 2021).

RoR: Read-over-Read for Long Document Machine Reading Comprehension

Keyphrases, that concisely summarize the high-level topics discussed in a document, can be categorized into present keyphrase which explicitly appears in the source text and absent keyphrase which does not match any contiguous subsequence but is highly semantically related to the source. Most existing keyphrase generation approaches synchronously generate present and absent keyphrases without explicitly distinguishing these two categories. In our work, a Select-Guide-Generate (SGG) approach is proposed to deal with present and absent keyphrase generation separately with different mechanisms. Specifically, SGG is a hierarchical neural network that consists of a pointing-based selector at a low layer concentrated on present keyphrase generation, a selection-guided generator at a high layer dedicated to absent keyphrase generation, and a guider in the middle to transfer information from selector to generator. Furthermore, we extend SGG to a title generation task which indicates its extensibility in natural language generation tasks.

SGG: Learning to Select, Guide, and Generate for Keyphrase Generation

Findings Papers - Question Answering

technical paper

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EMNLP 2021

EMNLP 2021 is planned to be a hybrid event in Punta Cana, Dominican Republic, with both on-site and fully virtual participation possible.



<i>**Session Chair: ** Siliang Tang </i><br><br>

16B-Oral: Information Extraction

2021 Annual Conference of the North American Chapter of the Association for Computational Linguistics.

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RoR: Read-over-Read for Long Document Machine Reading Comprehension

SGG: Learning to Select, Guide, and Generate for Keyphrase Generation

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