![Abstract
• Beam Search & Greedy Strategy
• Improvement vs. Cost
• Actor → decoder.hidden_state[t]
• Train: K outputs of beam search, argmax with a target quality
metric like BLEU (pseudo-parallel corpus / base model);
• No reinforcement learning and universal;
• Experiments: 3 corpora & 3 architectures [Q↑ & S↑]](data:image/gif;base64,R0lGODlhAQABAIAAAAAAAP///yH5BAEAAAAALAAAAAABAAEAAAIBRAA7)
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N20190116
- 1. 20190116 陈宙斯
- 2. Abstract • Beam Search & Greedy Strategy • Improvement vs. Cost • Actor → decoder.hidden_state[t] • Train: K outputs of beam search, argmax with a target quality metric like BLEU (pseudo-parallel corpus / base model); • No reinforcement learning and universal; • Experiments: 3 corpora & 3 architectures [Q↑ & S↑]
- 3. Intro • Seq-seq: conditioned left-to-right • Infinite and exponential with seq_len • Greedy & Beam: 2+BLEU | 3+ROUGE • Related: termination criterion & search function • Train: ordinary BP on a model-specific corpus • Corpus: generated by running the un-augmented model on the training set with large-beam beam search, and selecting outputs from the resulting k-best list which score highly on our target metric. • Evaluation: • RNN-based (Luong et al., 2015), ConvS2S (Gehring et al., 2017) and Transformer (Vaswani et al., 2017) • IWSLT16 De-En, WMT15 Fi-En and WMT14 De-En
- 4. Background • 2.1 NMT • 2.2 Decoding • Greedy (1); Beam (K) • Noisy parallel approximate decoding (NPAD; Cho, 2016) Noice → decoder.hidden_state[t] [idea] Stay active, even random! (better study at home cafe) • Trainable Greedy Decoding (Gu et al., 2017) FFNN RL actor → decoder.hidden_state[t] (やっぱり at lab!) approximate the maximum-a-posteriori → BLEU [不不安定､残念]
- 5. Method • I/O: at = actor(ht, et, st-1) ← dec/att/(state) • Form of actor: ff(5), ff2(6), GRU(7), gated ff(gate(8)) …
- 7. Training • Pseudo-parallel corpus generated by a base model: • High model likelihood (not highest it) • High-quality translations (not highest xt) • Gen: K beams → highest internal argmax external score • Train the actor with pseudo-D and fixed base model
- 8. Experiments • 4.1 Settings • IWSLT16, tst2013(validation) and tst2014(test), • WMT15, newstest2013(validation) and newstest2015(test) • WMT14, newstest2013(validation) and newstest2014(test) • + BPE • evaluations • tokenized and cased BLEU (primary). • METEOR and TER, multeval with tokenized and case-insensitive scoring. • Base models are trained from scratch, except for ConvS2S WMT14 En-De translation (trained model as well as training data) provided by Gehring et al. (2017). RNN: OpenNMT’s default, Luong rnn, emb = [500, 500; 600, 300] ConvS2S: IWSLT16 and WMT Transformer: Gu et al. (2018) (Pseudo-D beam k = 35)
- 11. recover missing tokens optimize word order also “correct prepositions” both
- 12. Two Questions • Two factors: actor & pseudo-D, which one matters? • Silver standard is a better choice than golden one? (Pseudo-D seems much more kind to the little driver/actor)
- 13. Impact Likelihood (conditioned LM)Magnitude of Action Vector L2 norms over the training course on the IWSLT16 De-En validation set with Transformer. This suggests that the action adjusts the decoders hidden state slightly, rather than overwriting it, enabling to find a sequence that is not highly scored but corresponding to a high value of the target metric. (more confident)
- 14. Actor & Data Yes, silver data is the best. Even bronze is better than gold!
- 15. Metric Domain?
- 16. 感想 • Modification of network: • Elements & Structure (organs of body) • A little guy on the shoulder of a blind giant (Transformer). • Contextual Parameter Generator: language embedding • The power of data seems much more greater than the elaborative work of network. A little actor can make the giant more flexible.