Conversational Response Re-ranking
Based on Event Causality and Role
Factored Tensor Event Embedding
S h o h e i Ta n a k a 1 , K o i c h i r o Yo s h i n o 1 2 , K a t s u h i t o S u d o h 1 , S a t o s h i N a k a m u r a 1
1N a r a
2 P R E S TO,

I n s t i t u t e o f S c i e n c e a n d Te c h n o l o g y
J a p a n S c i e n c e a n d Te c h n o l o g y A g e n c yIntroduction

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Neural Conversational Model (NCM)
RNNAreYouOK?

Context

I’m

notOK<eos>

<sos>

I’m

notOKResponse

NCM [Vinyals et al., 2015] can generate responses flexibly.
Often generates simple and dull responses.

I don’t know.

Users lose interest and finish dialogues.

I see.

Needs to maintain response coherency anddiversity to continue dialogues.
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Selecting Response Based on Event Causality
Re-ranks response candidates generated from NCM
based on event causality.
I’m stressed outrecently.

User

You are better torelieve stress.

SystemSelects a response with an event causality(“be stressed out” -> “relieve stress”) related
to the dialogue history.
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What is Event Causality?
Cause-effect relation between two eventse.g. be stressed out (cause) -> relieve stress (effect)
Used in why-QA system [Oh et al., 2013].
Why are tsunamis generated?

UserBecause earthquake
causes seismic waves.

SystemGenerates an answer related to the question based ona causality (“earthquake causes seismic waves” ->“tsunamis are generated”).
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Why is Event Causality Useful?
Selects a conversational response based on causality.

Event in the response is related to its dialogue history.
-> Coherency will be improved.
Response has a high mutual information.
-> Diversity will be improved.

Dialogue continuity will be improved.
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Response Re-ranking Using
Event Causality Relations

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Overview of Re-ranking…

1

1. I see.
2. I don’t know.
3. You must relax.

NCM

I’m exhausted.

…

Dialogue
History

Response Candidates
1. You must relax.
2. I see.
3. I don’t know.

2
Re-ranking
Function…
Re-ranked
Response Candidates

Event Causality Pairs(e.g. be stressed out ->relieve stress)

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Response Candidates Generation…

1I’m exhausted.

1. I see.
2. I don’t know.
3. You must relax.
…

Dialogue
History

NCM

Response Candidates

Generates response candidates
from a dialogue history.

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Re-ranking Based on Event Causality…

1. I see
I’m exhausted
2. I don’t know
Gives higher scores to response candidatesthat
3. You must
be relax
Dialogue…

have event causality relations to the dialogue history.
History
Response Candidates
1. You must relax.
2. I see.
3. I don’t know.

2Re-ranking
Function…
Re-rankedResponse Candidates“be exhausted” -> “relax” is used.

Event Causality Pairs(e.g. be stressed out ->relieve stress)

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Event Causality Pairs
Each event consists of a predicate and arguments.
Predicate: required, Argument: optional
Event Causality
Cause Event
PredicateArguments

be stressed out -Effect Event
PredicateArguments

relievestress

Uses event causality pairs to find causalities between
a dialogue history and response candidates.

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Coverage Problem of Event Causality Pairs
Event causality pairs do notinclude all causalities in dialogue

because they are obtained from limited Web corpus.
relieve stress…
Response Events(included)

Event Causality Pairs(e.g. be stressed out ->relieve stress)

relax



…
Response Events(not included)

An event in the dialogue (“relax”) can not
be found if it is not included in the pairs.
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Matching Based on Event Embedding
Finds a similar event causality pair on vector space.
…

be exhausted…

Dialogue History
Events (Embedded)…

Event Causality Pairs(Embedded)
be stressed out…

relieve stress

…

…

relax

Response Events (Embedded)

Similarity

A causality in the dialogue (“be exhausted” -> “relax”)
is found if a similar causality (“be stressed out” ->“relieve stress”) is included in the pairs.
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Role Factored Tensor Model (RFTM) [Weber et al., 2018]
Converts events to distributed representations based on
the relationship between a predicate and arguments.
relievepredicate
II relieve
T…

W1

argument 1+relieve
predicatestressrelieve stress
T…

I relieve stress…

W2

argument 2

Captures the specific meaning of the predicate.
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Experiments

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Experiment Settings

Setting

NCM

EncDec, HRED

Re-ranking 1-best (w/o re-ranking),
w/o embedding, w/ embedding
Data
2.6 million Twitter dataset(60 thousand test data)

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Re-ranked ratio of response candidates
Indicates how much re-ranking is applicable.
Re-ranking

NCM

w/o embedding
w/ embedding

Re-ranked

EncDecHRED
EncDec
HRED

12 %
70 %

Ratios were improved drastically by introducing
the event embedding method.

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Dist and Pointwise Mutual Information (PMI)Dist and PMI indicate diversity and coherency
NCM

Re-ranking

dist-1

dist-2PMI

1-bestEncDec w/o embedding
w/ embedding
1-bestHRED

w/o embedding
w/ embeddingModels with the embedding have the highest scores.
Diversity (dist) and coherency (PMI) were improved.
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NCM Used in Human Evaluation

Baseline model: HREDv.s.
Our models:
HRED-based models that re-rank w/o or w/ embedding

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Human Evaluation
Ten crowd-workers compared hundred responses
selected by two of three models in the two criteria.
• Word coherency
Which words in a response are more related to a dialogue history.

• Dialogue continuityWhich response is easier to respond to.

To reduce the workload, we removed the following data.
•

Number of user utterances is more than two.

•Needs external knowledge to evaluate.
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Word Coherency
w/o embedding
Coherency

28.62
0%

1-best

37.47

20%

40%

neither

33.91
60%

80%

100%

Re-ranking w/o embedding

w/ embedding
Coherency

30.10
0%

1-best20%

neither

44.50
40%

60%

25.40
80%

100%

Re-ranking w/ embedding

Word coherency was improved by our model without
the embedding, but worsened with the embedding.
Event embedding over-generalized events.
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Dialogue Continuity
w/o embedding
Continuity

40.84
0%

1-best

20%

20.63
40%

neither

60%

38.53
80%

100%

Re-ranking w/o embedding

w/ embedding
Continuity

35.50
0%

1-best20%

neither

26.30
40%

38.20
60%

80%

100%

Re-ranking w/ embedding

Dialogue continuity was improved by our model withthe embedding because dull responses were reduced.
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Case Study

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Appropriate Re-ranking

Conversation 1:
User: Because of my fears, I have been stressed out.
System (1-best): Are you OK?
System (Re-ranked): Are you OK? Don’t work too hard.

Appropriate causality, “work too hard” -> “be stressedout,” was used to select a coherent response.

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Over-generalization
Conversation 2:
User: Our president gives me beer, but he can’t drink…
System (1-best): I see.
System (Re-ranked): I drink.

“drink” -> “can drink” was obtained by generalizing“enter restaurant” -> “order beer”, which is included in the pairs.

Event embedding over-generalized events.
Need to prevent over-generalization by improving theembedding architecture.
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Lack of Response Naturalness
Conversation 3:
User: I can’t get up… I’m gonna take a day off… I have a fever…
System (1-best): Take care of yourself.
System (Re-ranked): You must be tired.

Proposed method focuses on only word coherency(“be tired” -> “take a day off”).
It is not natural.
Need to maintain the response naturalness.
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Conclusion

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Conclusion
We proposed a method to select coherent and diverse
responses based on event causality.
Proposed method improved coherency, diversity, anddialogue continuity by re-ranking.
I have been stressed out.

User

Are you OK? Don’t work too hard.

SystemFuture Work•

Updating the event embedding•

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Appendix

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Details of Re-ranking
Dialogue History…
I’m exhausted.

1

1. You must relax.
2. I see.
3. I don’t know.

Event Causality Pair Pool
(Embedded)
…… ->
……
()

…

Event Causality PairPool(be stressed out-> relieve stress)

1. I see.
2. I don’t know.
3. You must relax.
…

…

Response
Candidates

RFTM

Dialogue History Events…
Response Events……

……
Wikipedia +News Articles

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…

relax

Dialogue History
Events (Embedded)

…

…

3

be exhausted

2

Re-ranking
Function

Dialogue
Corpus

NCM

KNP

4

…

Re-ranked
ResponseCandidates

Response Events(Embedded)

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Similarity Scores to References• BLEUN-gram coincidence rate of references and generated responses.
Actual responses are coherent to dialogue histories.
BLEU correlates with response coherency to some extent.

• NIST
Based on BLEU, but heavily weights less frequent N-grams tofocus on content words.

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Similarity Scores to References (Cont.)• Vector Extrema
Cosine similarity between sentence vectors of a reference anda generated response.
Each sentence vector es is computed by taking extrema of
Skip-gram word vectors ew in each dimension d as,Copyright © 2019 Shohei Tanaka All Rights Reserved

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BLEU, NIST, and extrema
NCM

Re-ranking

BLEU

NISTextrema

1-bestEncDec w/o embedding
w/ embedding
1-best
HREDw/o embedding
w/ embedding

Re-ranking worsened similarity scores to the references.
NCMs generate similar responses to the references.
1-best responses should have the highest scores.
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Diversity/coherency evaluation• Dist-1, 2
Ratio of distinct N-grams in all responses.
Indicates response diversity.

• Pointwise Mutual Information (PMI)

Indicates response coherency.

Word in a dialogue history

Word in a response

Dists and PMI are unrelated to references.
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Summary of Experimental Results

In the human evaluation…
•Word coherency was improved.

•Dialogue continuity was improved.

Diversity (dists) and Coherency (PMI) were also
improved in the automatic evaluation.

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