This document summarizes a paper on using dual learning for semantic parsing without supervision. The key points are: 1) The paper proposes using dual learning to train semantic parsers without supervision by having two agents that translate between natural language queries and logical forms and provide feedback to each other. 2) The dual learning approach uses an attention-based encoder-decoder architecture for the primary tasks of translating queries to logical forms and vice versa, along with techniques like copying and entity mapping. 3) The dual agents are trained using reinforcement learning with rewards based on validity of the translations as judged by a logical form checker and language model, without any parallel data supervision. 4) Experiments on semantic parsing datasets like ATIS

1. ACL2019
Intro. by Chen

2. Our Goals
• Understanding the task
• Semantic Parsing & Logical Forms
• Understanding their method
• Dual Learning
• How to fit semantic parsing into dual learning
• Understanding other methods

3. Semantic Parsing
• Semantic Parsing:
• Mapping a natural language query into a logical form;
• Logical form:
• One type of meaning representation understood by
computers, usually executable to obtain answers.
• Common treatment:
• End-to-end Seq2seq models (fully supervised)

4. Example
• Query: “show flight from ci0 to ci1”
• Lambda expression:
• Strictly structured & executable
• A flexible function instance expressed by strings
• Foundation of programming languages » check wiki
• Demand of semantic parsing:
• Valid and complete at surface and semantic levels.
( lambda $0 e
( and
( flight $0 )
( from $0 ci0 )
( to $0 ci1)
) )

5. Task Demand• Surface:
• A complete tree structure / logical form
• “()” matches
• Semantic:
• Predicate & its arguments (or even their types) match
• ( flight $0 ) (from $0 ci0) (to $0 ci1)
• i.e. semantic parsing is (NL-to-AMR/SQL)
• A bridge between human and machine languages
• Python is good interpreter | we are semantic parsers.

6. A semantic parser needs to …
• (below are in my words)
• Understand the natural language | its meaning.
• Able to infer its structure.
• Understand the function and its arguments.
• Able to translate / map.
• btw. MT symbols -> symbols ⼈人
👀

7. This work
• Dual learning into semantic parsing
• Query to logical form (Q2LF);
• Logical form to query (LF2Q);
• Benefit or trait:
• No supervision
• Reinforcement learning: a validity reward for
• Achievement:
• SOTA on ATIS | competitive SOTA on OVERNIGHT
↕ provide signals to each other
Surface
Semantic
(primary)
(dual)

8. Primary Task
• Attention-based Encoder-Decoder architecture
• (Luong et al., 2015) attention:
• Regular endec output:
• Copy mechanism
• Entity Mapping: Uniform Resource Identifier (URI, K&C, 2006)
• e.g. kobe bryant → en.player.kobe_bryant;
•
gt ⋅
(1 − gt) ⋅
+
Selection at t
Attentions at t

9. Dual Model
• Reverse Entity Mapping:
• e.g. en.player.kobe_bryant → [the black mamba; …]
• Randomly select on from the list.
KB−1
(yt)

10. Dual learning
• Two agents & two loops:
• (Q2LF, LF2Q), (LF2Q, Q2LF)
• Reinforcement learning based on policy gradient (Sutton)
• Data:
• queries； logical forms； parallel
• Supervised initialization with ;
• Two unsupervised loops with 、
𝒬 ℒℱ 𝒯
𝒬 ∪ 𝒯
𝒬 ∪ 𝒯 ℒℱ ∪ 𝒯

11. Supervisor Guidance
• When is limited, the unsupervised models would rot.
• Initial training: maximum likelihood estimation (MLE)
• Train Q2LF and LF2Q on .
• Other preparation:
• Train on .
• Logical form checker: grammar_error_indicator(･)
𝒯
𝒯
LMq 𝒬 ∪ 𝒯

12. Loop starts from a query
1. Sample a query from ;
2. Q2LF generates k logical forms with beam search;
3. Calculate validity reward:
4. Reconstruct with LF2Q
5. Calculate reconstruction reward
6. Balance rewards:
7. Update:
𝒬 ∪ 𝒯
y1, y2, ⋯yk
Rval
q (yi)
Rrec
q (x, yi)
rq
i
= αRval
q (yi) + (1 − α)Rrec
q (x, yi)

13. Loop starts from a logical form
1. Sample a query from ;
2. LF2Q generates k queries with beam search;
3. Calculate validity reward:
4. Reconstruct with Q2LF
5. Calculate reconstruction reward
6. Balance rewards:
7. Update:
ℒℱ ∪ 𝒯
x1, x2, ⋯xk
Rval
lf (xi)
Rrec
lf (y, xi)
rlf
i
= βRval
lf (xi) + (1 − β)Rrec
lf (y, xi)

14. Reward design (1/2)
• grammar_error_indicator(･)
has been included in
OVERNIGHT dataset.
• Otherwise, construct a
grammar_error_indicator(･)
based on the ontology of
the corresponding dataset.
• 1 when correct, otherwise 0
• Rval
q (y) = grammar_error_indicator(y)

15. Reward design (2/2)
• The pre-trained language model for queries:
• ;
• That’s it!
• Let’s see experiments.
LMq
Rval
lf =
log LMq(x)
Length(x)

16. Experiments
• Datasets (supervised)
• ATIS(1994):
Airline
Travel
Information
System
• OVERNIGHT(2015):
• 8 domain: calenda/
restaurants/social
networks/
basketball/blocks/
publication/
recipes/housing
Parallel data (Two s)𝒯

17. Experiments
• Synthesis logical forms
( for ATIS )
• Modification based
on ontology:
• Replace entity or
predicate;
• Check validity;
• Check novelty;
Data augmentation (1/2)ℒℱ

18. Experiments
• Synthesis logical forms (for OVERNIGHT)
• Generation based on grammar:
• Reorder entity instances of one type.
• +500 for each domain.
Data augmentation (2/2)ℒℱ

19. Experiments
• Base model:
• and : 0.5
• Pre-trained word: Glove6B
• : 100-dim word emb
200-dim hidden
• Training and decoding:
• Beam size: {3, 5}
• Batch size: {10, 20}
• Adam lr: 0.001
α β
LMq
Model settings
• Pseudo baseline, 1/2 loss:
• Back-translation (Sennrich
et al., 2016)
• “Varied Queries” (Guo et
al., 2018)

20. ATIS
陳：Results on ATIS
are convincing.
Simple data
augmentation does
not help the model.
Back-trans fails to
treat SQL well.
DUAL dramatically
makes their model
so different.
Both Copy-mech &
Coarse2Fine
suggest the sparsity
of natural language.

21. OVERNIGHT
OVERNIGHT contains less distinct entities and training samples,
that copy is not essential.
陳：I start to wonder, this is the maximum of the Seq2seq capacity.

22. Ablation (1/4)
• Cut into:
• 50% ; 50% ; 50%
𝒯
𝒯 𝒬 ℒℱ
is still essential;
↓
𝒯
Semi-supervised
prev. ATTPTR+DUAL+LF | 79.9 | 89.1

23. Ablation (2/4)
←
is still essential;
(On ATIS)
𝒯
Ratio of labeled data

24. Ablation (3/4)
(On ATIS)
• Cut into:
• 30% ;
• 70% ;
• 70% .
𝒯
𝒯
𝒬
ℒℱ
more unlabeled data
DUAL
MLE

25. Ablation (4/4)
Choice for validity reward
Soft
Hard
Soft
Hard

26. 感想（⼀一⾔言）
Untrained model
Model trained without MLE
Rewards
Model trained with MLE
Loss
Model explored with RL