Conversational Response Re-ranking Based on Event Causality
and Role Factored Tensor Event Embedding
Shohei Tanaka1 , Koichiro Yoshino1,2 , Katsuhito Sudoh1 , Satoshi Nakamura1
1
Nara Institute of Science and Technology
2
PRESTO, Japan Science and Technology Agency{takana.shohei.tj7, koichiro, sudoh, s-nakamura}@is.naist.jp

Abstract
We propose a novel method for selecting coherent and diverse responses for a given dialogue context. The proposed method re-ranks
response candidates generated from conversational models by using event causality relations between events in a dialogue history
and response candidates (e.g., “be stressedout” precedes “relieve stress”). We use distributed event representation based on the Role
Factored Tensor Model for a robust matching of event causality relations due to limited event causality knowledge of the system. Experimental results showed that the proposed method improved coherency and dialogue continuity of system responses.

1

Introduction

While a variety of dialogue models such as theneural conversational model (NCM) (Vinyals andLe, 2015) have been researched widely, such dialogue models often generate simple and dull responses due to the limitation of their ability to takedialogue context into account. It is very difficult
for these models to generate coherent responses toa dialogue history. We tackle this problem with a
new architecture by incorporating event causality
relations between response candidates and a dialogue history. Typical event causality relations arecause-effect relations between two events, such as“be stressed out” precedes “relieve stress.” In thispaper, event causality relations are defined that aneffect event is likely to happen after a corresponding cause event happens (Shibata and Kurohashi,
2011; Shibata et al., 2014). Event causality relations have been used in why-question answeringsystems to focus on causalities between questions
and answers (Oh et al., 2013, 2016, 2017). It isalso reported that a conversational model using
event causality relations can generate diverse andcoherent responses (Fujita et al., 2011). However,

the relation between dialogue continuity and thecoherency of system responses is still an underlying problem.
In this paper, we propose a novel method to select an appropriate response from response candidates generated by NCMs. We define a score forre-ranking to select a response that has an event
causality relation to a dialogue history. Re-ranking
effectively improves response reliability in language generation tasks such as why-question answering and dialogue systems (Oh et al., 2013;
Jansen et al., 2014; Bogdanova and Foster, 2016;
Ohmura and Eskenazi, 2018). We used event
causality pairs extracted from a large-scale corpus (Shibata and Kurohashi, 2011; Shibata et al.,2014). We also use distributed event representation based on the Role Factored Tensor Model(RFTM) (Weber et al., 2018) to realize a robust
matching of event causality relations, even if thesecausalities are not included in the extracted event
causality pairs. In human and automatic evaluations, the proposed method outperformed conventional methods in selecting coherent and diverse
responses.

2 Response Re-ranking Using Event
Causality Relations
Figure 1 shows an overview of the proposed
method. The process consists of four parts. First,
N -best response candidates are generated from1
an NCM given a dialogue history (Figure 1 ⃝;Section 2.1). Then, events (predicate-argument
structures) are extracted by an event parser fromboth the dialogue history and the response candi2
dates (Figure 1 ⃝).
We used Kurohashi Nagao
Parser (KNP)1 (Kawahara and Kurohashi, 2006;
Sasano and Kurohashi, 2011) as the event parser.
Next, the extracted events are converted to dis1

http://nlp.ist.i.kyoto-u.ac.jp/?KNP

Figure 1: Neural conversational model+re-ranking using event causality; a response that has an event causality
relation (“be exhausted” → “relax”) to the dialogue history is selected by the re-ranking.
predicate 1
be stressed out

argument 1-

predicate 2
relieve

lif t
10.02

argument 2
stressTable 1: Example of event causality relations included in event causality pairs

tributed event representations by an event embed3
ding model (Figure 1 ⃝;Section 2.3). Eventsin event causality pairs are also converted to distributed representations to calculate similarities.
The RFTM is used for the embedding. Finally, re4 Secsponse candidates are re-ranked (Figure 1 ⃝;tion 2.2, 2.4). We describe these components inmore detail below.
2.1 Neural Conversational Model (NCM)
NCM learns a mapping between input and output word sequences by using recurrent neural networks (RNNs). NCMs can generate N -best response candidates by using beam search or sampling (Macherey et al., 2016).
2.2 Event Causality Pairs
The proposed method uses event causality pairs.
Events in a pair, which have cause-effect relations,are extracted from a large-scale corpus on the basis of co-occurring statistics and case frames (Shibata and Kurohashi, 2011; Shibata et al., 2014).
420,000 entries are extracted from 1.6 billion
texts: each entry consists of information denotedin Table 1. “predicate 1” and “argument 1” arecomponents of a cause event, and “predicate 2”
and “argument 2” are components of an effectevent. Each event consists of a predicate and arguments. The predicate is required, and the argument
is optional. We used arguments that have the following roles: nominative, accusative, dative, instrumental, and locative cases. lif t is the mutual

information score between two events, which indicates the strength of the causality relation. Using
lif t, we propose a score for re-ranking as,score = max (<e ,e >
hr

log2 p)λ .
log2 lif t(eh , er )(1)

p is the posterior probability of the response candidate provided by NCM. λ is a hyper parameter to decide the weight of event causality relations. lif t(eh , er ) is the lif t score between an
event eh in the dialogue history, and an event er
in the response candidate, which is equal to 2 if
the pair does not appear in the extracted event
causality pair pool. Note that lif t(eh , er ) is logscaled because it has a wide range of values (10 <lif t(eh , er ) < 10, 000). In the case where morethan one event causality relations are recognized
between the dialogue history and the response candidate, the score of the candidate is determined by
the relation with the highest lif t(eh , er ). We callthis model “Re-ranking.”
2.3 Distributed Event Representation Basedon Role Factored Tensor Model (RFTM)
It is difficult to determine event causality relations
by using only the pairs observed in an actual corpus. Therefore, we introduce a distributed event
representation to improve the robustness of matching events in a dialogue with those in the event
causality pair pool. Any events are embedded into
fixed length vectors to calculate their similarities.

Figure 2: Model architecture of predicate embedding

We define an event with a single predicate ora pair of a predicate and arguments. Argument aof an event is embedded into vector as va by using
Skip-gram (Mikolov et al., 2013c,a,b). Predicate pof an event is embedded into vector as vp by using
predicate embedding which is based on case-unit
Skip-gram. Figure 2 shows the model architectureof predicate embedding. The model learns predicate vector representations which are good at predicting its arguments. To get an event embedding
for the pair of vp and va , we propose to use RFTM,
which was proposed by Weber et al. (2018). The
RFTM embeds a predicate and its arguments into
vector e as,∑e=
Wa T (vp , va ).
(2)
a

The relation of a predicate and its arguments iscomputed using a 3D tensor T and matrices Wa . If
the event has no arguments, e is substituted by vp .
The RFTM is trained to predict an event sequence;thus it can represent the meaning of the event in aparticular context.
2.4 Event Causality Relation Matching Basedon Distributed Event Representation
Figure 3 illustrates the process of matching eventson the basis of distributed event representation.
Given an event pair from a response candidate
and a dialogue history, the proposed method finds
an event causality pair that has the highest cosine
similarity from the pool. lif t score, strength of theevent causality relation, is extended as,
lif temb (eh , er ) =()
lif t(ec , ee ) ∗ mean sim(eh , ec ), sim(er , ee ) .
(3)

eh is an event in the dialogue history, er is an
event in the response candidate. ec and ee are respectively a cause and an effect event of an event

Figure 3: Event causality relation matching; the lif tof the event causality relation in which “be exhausted”
precedes “relax,” is calculated from the lif t of the mostsimilar event causality relation where “be stressed out”
precedes “relieve stress.”
Ave.dist-1
0.44
0.33

EncDecHRED

Ave.dist-2
0.56
0.42

Table 2: Diversity of N -best Response Candidatescausality pair. We also calculate the score for thecase in which the cause and effect events are exchanged to deal with the inverse case. Note thatboth sim values have a threshold to prevent overgeneralization.
The threshold was empirically de√cided as 3/2. Replacing lif t(eh , er ) in Eq. (1)with lif temb (eh , er ), the score using distributed
event representation is defined as,score = max (<e ,e >
hr

log2 p)λ .
log2 lif temb (eh , er )(4)

We call this model “Re-ranking (emb).”

3 Experiments
We conducted automatic and human evaluations
to compare responses with and without the reranking. We evaluated our proposed re-ranking
method on a conventional Encoder-Decoder with
Attention (EncDec) model (Bahdanau et al., 2015;
Luong et al., 2015) and a Hierarchical Recurrent
Encoder-Decoder (HRED) model (Sordoni et al.,2015; Serban et al., 2016). While HRED tries togenerate more coherent responses to dialogue context than a simple Encoder-Decoder, the diversityof responses is small due to context constraints.
We used the Japanese data from a Wikipedia
dump for training Skip-gram and predicate wordembeddings of RFTM, and the Maichichi newspaper dataset 20172 for training RFTM. We col2
http://www.nichigai.co.jp/sales/mainichi/mainichidata.html

Method
NCM
reference
EncDecEncDecEncDecEncDecEncDec
HRED
HRED
HRED
HRED
HRED

history
1
1
5
5
1
1
5
5

re-ranking
1-bestRe-ranking
Re-ranking (emb)Re-ranking
Re-ranking (emb)1-bestRe-ranking
Re-ranking (emb)Re-ranking
Re-ranking (emb)Evaluation
re-ranked (%)
4,016 (7.90)
29,343 (57.71)
6,469 (12.72)
35,284 (69.39)
3,671 (7.22)
30,992 (60.95)
6,231 (12.25)
36, 373(71.53)

BLEU
1.12
1.10
1.02
1.09
1.00
1.34
1.33
1.28
1.33
1.28

NIST
1.19
1.18
1.07
1.17
1.04
2.74
2.74
2.74
2.73
2.74

extrema
0.42
0.42
0.40
0.42
0.39
0.42
0.42
0.41
0.42
0.41

dist-1
0.06
0.06
0.06
0.06
0.06
0.07
0.07
0.06
0.06
0.06
0.06

dist-2
0.40
0.18
0.19
0.20
0.19
0.21
0.20
0.20
0.20
0.20
0.20

PMI
1.86
1.77
1.78
1.77
1.78
1.77
1.84
1.84
1.86
1.84
1.86

length
21.43
15.55
15.52
15.64
15.50
15.66
35.05
35.20
34.80
35.30
34.60

Table 3: Comparison results before and after re-ranking

lected 2,632,114 dialogues from Japanese microblogs (Twitter) to train and test the dialogue models. The average dialogue turn was 21.99, and theaverage utterance length was 22.08 words. We removed emoticons from utterances to reduce vocabulary size and accelerate the training. The dialogue corpus was split into 2,509,836, 63,308, and58,970 dialogues as training, validation, and testing data, respectively.
3.1 Model Settings
The hidden unit size of Skip-gram (Mikolov et al.,2013c,a,b), predicate embedding, and RFTM (Weber et al., 2018) was 100. We used gated recurrent units (GRUs) (Cho et al., 2014; Chung et al.,2014) whose number of layers was 2 and hidden
unit size was 256, for the encoder and decoder of
the NCMs. The batch size was 100, the dropout
probability was 0.1, and the teacher forcing rate
was 1.0. We used Adam (Kingma and Ba, 2015)
as the optimizer. The gradient clipping was 50,
the learning rate for the encoder and the context
RNN of HRED was 1e−4 , and the learning rate forthe decoder was 5e−4 . The loss function was inverse token frequency (ITF) loss (Nakamura et al.,2019). We used sentencepiece (Kudo and Richardson, 2018) as the tokenizer, and the vocabulary
size was 32,000. These settings were the same inall models.
Repetitive suppression (Nakamura et al., 2019)and length normalization (Macherey et al., 2016)
were used at the decoding step. Finally, λ of Eq.
(1) and Eq. (4) was set to 1.0.

following experiments.
The result is shown in Table 2: Ave.dists areaverages of dist computed internal N -best response candidates. The diversity of EncDec ishigher than that of HRED.
3.3 Comparison in Automatic Metrics
Table 3 shows the results of our evaluation using automatic metrics. We compared the resultsby referring to the ratio of responses different
from the without re-ranking method (“re-ranked”),
bilingual evaluation understudy (BLEU) (Papineni
et al., 2002), NIST (Doddington, 2002), and vector
extrema (Gabriel et al., 2014) (“extrema”) score.
NIST is based on BLEU, but heavily weights lessfrequent N-grams to focus on content words. Vector extrema computes cosine similarity between
sentence vectors of a reference and a generated response from a model. Each sentence vector es iscomputed by taking extrema of Skip-gram word
vectors ew in each dimension d as,{
maxw∈s ewd if ewd > | minw′ ∈s ew′ d |esd =.
minw∈s ewd otherwise(5)
esd and ewd are the dth dimensions of es andew respectively. Additionally, we evaluated dist
(Li et al., 2016), Pointwise Mutual Information(PMI) (Newman et al., 2010), and average response length (“length”). Dist and PMI are used
to evaluate diversity and coherency respectively.
PMI between a response and a dialogue history isdefined as,

3.2 Diversity of Beam Search
We investigated internal diversity of N -best response candidates generated from each dialogue
model. It is expected that the higher diversity is,
the more effective re-ranking is. Hence, we evaluated diversity on the test data by dist-1, 2 (Li et al.,2016). Beam width was set to 20; it is same in the

PMI =

1
|response||response|∑wr

max PMI(wr, wh).
wh(6)
wr and wh are words in the response and the dialogue history respectively. Each method used a1-bestRe-ranking
neitherword
coherency
28.62
33.91
37.47

dialoguecontinuity
40.84
38.53
20.62

Table 4: 1-best v.s. Re-ranking; # dialogues is 100.

1-bestRe-ranking (emb)neitherword
coherency
30.10
25.40
44.50

dialoguecontinuity
35.50
38.20
26.30

Table 5: 1-best v.s. Re-ranking (emb); # dialogues is100.

specific NCM, a range of dialogue history used for
re-ranking, and re-ranking method. Methods with
“1-best” used neither re-ranking and event embedding. Those with “Re-ranking” used re-ranking
but did not use event embedding. Those with “Reranking (emb)” used both the re-ranking and the
proposed event embedding method.
Re-ranking lowered scores of the similarity toreference: BLEU, NIST, and extrema, because
normal NCM models were trained to generate similar responses to the references, generated top 1
response before re-ranking should have the highest scores in those similarity metrics. Dist-2 andPMI were improved by re-ranking. This indicatesthat words in re-ranked responses are diverse andcoherent to dialogue histories. However, ratios ofre-ranked responses were around 10%; hence, theeffect of re-ranking was limited. By introducing
the proposed event embedding method, the ratiosof re-ranked responses improved drastically (Reranking vs. Re-ranking (emb)). Moreover, the reranking models with event embedding have highest dist-1, dist-2, and PMI. As the HRED models had higher BLEU, NIST, and PMI values thanthose of EncDec models in all re-ranking methods,
we conducted a human evaluation by comparing
HRED model-based systems.
3.4 Human Evaluation
It is difficult to evaluate system performances onlywith automatic metrics (Liu et al., 2016). Hence,
we compared a baseline model and our models
in a human evaluation to confirm coherency anddialogue continuity of responses selected by our
proposed methods. We compared baseline HRED
model with our proposed models, re-ranked without embedding and with embedding using the last

Re-ranking
Re-ranking (emb)neitherword
coherency
23.70
22.91
55.39

dialoguecontinuity
35.53
35.65
28.83

Table 6: Re-ranking v.s. Re-ranking (emb); # dialogues
is 100.

five histories. To reduce evaluators’ workload, weused test data whose the number of user utterances is less than three, and removed dialogues
which need external knowledge to evaluate. We
used crowdsourcing for the human evaluation. Ten
crowd-workers compared responses selected by
two of three models in the following two subjective criteria. The first one is “which words
in a response are more related to a dialogue history” (word coherency), which indicates system
response coherency to dialogue histories. The second criterion is “which response is easier to respond to” (dialogue continuity), which indicates
how much dialogue continuity system responses
have. We were inspired to make these criteria by
those of the Alexa Prize (Ram et al., 2018).
The results are shown in Table 4, 5, and 6. Word
coherency was improved by our model without
embedding, but lowered by the model with embedding. This is because workers acknowledgedcausality relations included in the event causality pair pool, but did not acknowledge generalizedcausalities with event embedding. However, dialogue continuity was improved by the proposed
re-ranking model with embedding, it is probably
because the proposed model reduced the numberof dull responses. We need to investigate the better threshold in the event embedding to balanceout the coherency and the continuity as the futurework.

4 Discussion
We analyzed an adequacy of re-ranking using
event causality relations. Here are system response examples of our proposed method. “()” indicates original Japanese sentences, “[]” indicates
event causality relations used for re-ranking, and“<>” indicates responses before re-ranking. All
examples are translated from Japanese to English.
Conversation 1:
User 1: Because of my fears, I havebeen stressed out.

(Mou fuan-na koto ga oosugite sutoresu
ga tamatteku.)
User 2 (System): Are you OK? Don’t
work too hard.
ß (Daijobu desuka muri shinaide kudasaine)[work too hard → be stressed out(muri wo suru → sutoresu ga tamaru)]<Are you OK? (Daijobu desuka)>(Osake wo nomenai shacho ga biru wo
kureru...)User 2 (System): I will drink alcohol.
(Osake nomu.)[drink alcohol → can drink alcohol(osake wo nomu → osake wo nomeru)]<I see. (wakaru)>

Conversation 5:
Conversation 2:
User 1: Someone, let’s go to eat luncharound Yayoi-ga-oka tomorrow. Everyone is welcome.
(Dareka, ashita, yayoi-ga-oka shuhen
de hiru-gohan tabe ni iko daredemo
iiken onegai)
User 2 : Let’s go. (Ikimasuka)
User 1: Where will we go?
(Doko kui iku)
User 2 (System): Anywhere near Shinjuku station(Shinjuku-eki no chikaku)[be near → go (chiaku → ikeru)]<Where are you? (doko inno)>
In above examples, appropriate event causality
relations are used to select logical, coherent, anddiverse responses. However, we found that thesecases are not majority. Our method used inadequate event causality relations even if coherent responses were selected as a result (“Conversation3”). Responses selected by our method are sometimes unnatural and incoherent than those before
re-ranking as “Conversation 4”, “Conversation 5”,
or “Conversation 6”.
Conversation 3:
User 1: I can’t stop coughing: ((Hen na seki ga tomaranai...namida)
User 2 (System): Don’t catch a cold.
(kaze hikanai dene)[catch cold → stop coughing(kaze wo hiku → seki ga tomaru)]<Are you OK? (daijobu-ka)>
Conversation 4:User 1: Our president who cannot
drink alcohol gives me beer...

User 1: Despite of morning, ambulances and police cars were noisy... I
was wondered what happened?
(Asa nanoni, kyu-kyu-sha to patoka no
oto ga sugoi...doshitandaro)
User 2 (System): Good morning.
(Ohayogozaimasu)[morning → good morning(asa → ohayo)]<Are you OK? (Daijobu desuka)>

Conversation 6:
User 1: I, I can’t get up... I’m gonnatake a day off... I have a fever...
(O, okiagarenai... kaisha yasumu...
netsu...)
User 2 (System): You must be tired.
(Otsukaresama desu)[be tired → take a day off(tsukare → yasumu)]<Take care of yourself. (Odaiji-ni)>

Considering the result of human evaluation andabove examples, we hypothesized that our method
have two problems in slecting appropriate event
causality relations. The first problem is that theevent embedding over-generalized events (“Conversation 4”). The causality in Conversation 4(“drink alcohol” precedes “can drink alcohol”) is
obtained by generalizing a causality that “enter
restaurant” precedes “order beer”, which is included in the event causality pair pool. It is necessary to prevent over-generalization by improving the embedding architecture. The second problem is that our method focuses on only word coherency, not response naturalness (“Conversation
5” and “Conversation 6”). To solve the problem,
our method has to maintain response naturalness
while improving coherency of word choices.

5

Conclusion

We proposed a selection of response candidates
generated from a neural conversational model(NCM) utilizing event causality relations. The
method had a robust matching of event causality relations attributed to distributed event representation. Experimental results showed thatthe proposed method selects a coherent and diverse response. The proposed method can beapplied to any languages that have a semantic
parser, because it uses predicate-argument structure based event expressions. However, unnatural
responses were sometimes selected due to inadequate event causality relations. Future work will
focus on solving the problem by preventing overgeneralization of events, and maintaining response
naturalness.

Acknowledgments
We would like to thank Sadao Kurohashi, Ph.D.
and Tomohide Shibata, Ph.D. of Kurohashi Laboratory in Kyoto University who provided us theevent causality pairs.
This work is supported by JST PRESTO (JPMJPR165B).

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