Task: Question Answering
Input (English): We compare our proposed discrete CVAE (DCVAE) with the two-stage sampling approach to three categories of response generation models:

Baselines: Seq2seq, the basic encoder-decoder model with soft attention mechanism BIBREF30 used in decoding and beam search used in testing; MMI-bidi BIBREF5, which uses the MMI to re-rank results from beam search.

CVAE BIBREF14: We adjust the original work which is for multi-round conversation for our single-round setting. For a fair comparison, we utilize the same keywords used in our network pre-training as the knowledge-guided features in this model.

Other enhanced encoder-decoder models: Hierarchical Gated Fusion Unit (HGFU) BIBREF12, which incorporates a cue word extracted using pointwise mutual information (PMI) into the decoder to generate meaningful responses; Mechanism-Aware Neural Machine (MANM) BIBREF13, which introduces latent embeddings to allow for multiple diverse response generation. 
 Question: What other kinds of generation models are used in experiments?
Output (English):  Seq2seq CVAE Hierarchical Gated Fusion Unit (HGFU) Mechanism-Aware Neural Machine (MANM)