End-to-End Answer Chunk Extraction and Ranking for Reading Comprehension

Yang Yu, Wei Zhang, Kazi Hasan, Mo Yu, Bing Xiang, Bowen Zhou

arXiv: 1610.09996


real-word QA: factoid and non-factoid

Non-factoid questions may or may not rise from a single document. RCQA task mainly deals with single document only.

previous RCQA work: entity or choice, answer boundary are trival or already given


  • quiz-style (MCTest, MovieQA): multi-choice questions
  • cloze-style: factoid question
  • answer-selection
    • TREC-QA: factoid question from multiple given passages
    • bAbI: for inference
    • SQuAD: both factoid and non-factoid, answer is a span in passage

this paper: arbitrary length, complexity: $O(n^2)$ (n possible start positions, max length n)

Chunk: a span in passage

Existing works for answer selection:

  • Rajpukar et al. 2016: extract candidates using rule -> ranking candidates using hand-crafted features
  • Wang and Jiang 2016: end-to-end predicting answer boundary or tagging answer/not-answer


  • joint candidate chunking and ranking
  • new question attention
  • some useful features

Dynamic Chunk Reader

training data:


constraints as filters


An improvement over (Rajpurkar et al. 2016): not to use rules

Chunking answers: POS patterns in training set (cover 90% questions in dev. set)

Ranking chunks:

  • Scoring the given passage:
    • word-level single-layer Gated Attention Reader (Dhingra et al. 2016): state-of-the-art on CNN/DayliMail
  • collect chunks score from the passage
  • extract features for the score sequence:
    • max / min / avg / sum
    • quantity of the matched POS patterns in the chunk (detail not given)
  • ranking: (Ganjisaffar, Caruana, and Lopes 2011) NDCG@10

useful info. to use previous works



Encoder Layer:

input concatenation:

  • GloVe embedding
  • one-hot POS encoding
  • one-hot NE tag
  • binary: whether word surface form is in question
  • binary: whether lemma of word is in question
  • binary: is capitalized or not

Bi-GRU (effective in RC and NMT: Bahdanau, Cho and Bengio, 2015; Kadlec et al. 2016; Dhingra et al. 2016):

  • shared parameters in question and passage (better than not shared)
  • concatenation of forward and backward

attention layer

beta is a weighted pooling of question hidden states

again, another Bi-GRU (and [forward;backward]) gives $v_j\to\gamma_j$

Chunk Representation

for Chunk boundary:

  • matched POS pattern
  • enumerate all possible chunks

chunk representation:

Ranker Layer

different from above, the last state in forward and first in backward

softmax is similar to list-wise ranking in (Cao et al. 2007)




  • POS-trie is good enough

Baseline EM upper bound:

  • 72% words with highest attention score are in correct span
  • 92% ground truth answer is in the candidate chunk set
  • upperbound is around 0.66 = 0.72 * 0.92
  • EM of DCR is at 62%

Accuracy of longer answer is worse.

  • question with long answer
  • question asking for reasons
  • question asking for explainations