The DCU Discourse Parser: A Sense Classification Task
Tsuyoshi Okita, Longyue Wang, Qun Liu
Dublin City University, ADAPT Centre
Glasnevin, Dublin 9, Ireland{tokita,lwang,qliu}@computing.dcu.ie

Abstract
This paper describes the discourse parsing
system developed at Dublin City University for participation in the CoNLL 2015
shared task. We participated in two tasks:a connective and argument identification
task and a sense classification task. This
paper focuses on the latter task and especially the sense classification for implicit
connectives.

1

Introduction

This paper describes the discourse parsing systemdeveloped at Dublin City University for participation in the CoNLL 2015 shared task (Xue et al.,2015). We participated in two tasks: a connective and argument identification task and a senseclassification task. This paper focuses on the lattertask.
We divide the whole process into two stages:
the first stage concerns an identification of triples(Arg1, Conn, Arg2) and pairs (Arg1, Arg2)
while the second stage concerns a sense classification of the identified individual triples and pairs.
The first phase of the identification of connective and arguments are described in (Wang et al.,2015), which bases on the framework of (Lin et al.,2009) and is also presented in this shared task as
a different paper. Hence, we omit the detailed description of the first stage (See (Wang et al., 2015)
for identification of connectives and arguments).
This paper focuses on the second stage which concerns sense classification.

2

Sense Classification

We use off-the-shelf classifiers with four kinds offeatures: relational phrase embedding, production,
word-pair and heuristic features. Among them,
we test the method which incorporates relational
phrase embedding features for Arg1 and Arg2 for

Implicit
ExplicitRelphrase
(2.1)
yes
yes

Prod
(2.2)
yes/no1
no

Word
pair
(2.3)
yes/no2no

Heuristic
feat
(2.4)
no
yes

Table 1: Overview of features used for implicit/explicit classification.
discourse parsing. Production features are proposed in (Lin et al., 2014) and word-pair features
are reported in (Lin et al., 2014; Rutherford andXue, 2015). Heuristic features, which is specificfor explicit sense classification, are described in(Lin et al., 2014).
We consider the embedding models which leadto two different types of intermediate representations. The relational phrase embedding model
considers the dependency within words uniformly
without considering the second-order effect. The
word-pair embedding model considers the secondorder effect of specific combinations within theword-pairs in Arg1 and Arg2. If we plug in aparagraph vector model for the relational phrase
embedding model, the model considers the effectof uni-gram within a sentence as a sequence. If
we plug in a RNN-LSTM model (Le and Zuidema,2015), the model considers the effect of uni-gramwithin a sentence as a tree.
2.1 Relational Phrase Embedding Features
Phrase embeddings (or sentence embeddings) aredistributed representation in a higher level than aword level. We used a paragraph vector model toobtain these phrase embeddings (Le and Mikolov,
2014). Upon obtained the phrase embeddings for1
For the official score, we did not use production featuresdue to the timing constraint. We write the result for the development set.
2
For the official score, we did not use the word-pair feature due to the timing constraint. We write the result for thedevelopment set.

Arg1, Arg2 (and Connectives), we used the relational phrase embedding from these triples (orpairs) based on their phrase embeddings (Bordes
et al., 2013).
The first type of embedding we used in thispaper is a combination of paragraph vector (Leand Mikolov, 2014) and translational embeddings(Bordes et al., 2013). First, the abstraction of eachvariable Arg1 and Arg2 was built independently
in a vertical way, and then the relation among
these (Arg1,Conn,Arg2) and (Arg1,Arg2) areexamined in a collective way. This is shown in
Figure 3. This model has two intermediate embeddings: paragraph vector embeddings of Arg1,
Arg2, and Conn, and translational embedding of(Arg1,Conn,Arg2) and (Arg1,Arg2).

Figure 2: Figure shows a scalability of implicit
classification performance based on the size of additional training data. We used dev set and used resources from WestBurry version of wikipedia corpus5 and WMT146 .

Relational phrase embedding model
sense classification
Contingency, Comparison, Temporal

relational phrase embeddings
triple = (Arg1, connective, Arg2)

intermediate level
on

on

on

word vectors
word vectors

word vectors

paragraphID

word vectors

theword vectors

word vectors

cat

sat

word vectors

word vectors

word vectors

word vectors

paragraph
ID

the

cat

sat

word level

Arg1

Arg2

country funds offer an easy way to geta taste of foreign stocks without thehard research of seeking out individualcompanies

it doesn’t take much to getburnedword vectors

word vectors

paragraphID

word vectors

theword vectors

are labeled collectively instead of individually labeled where each contains many instances. All themore, linguistic characteristics of discourse relations support this: meaning/sense is attached not
to a single argument Arg1 or Arg2 but to a pair
(Arg1, Arg2) or a triple (Arg1, Conn, Arg2).
Followed by Bordes et al. (Bordes et al., 2011;
Bordes et al., 2013), we minimized a marginbased ranking criterion over the pair of embeddings:
L =word vectors

cat

sat

connective

X

X

[γ +

(Arg1,Arg2)∈S (Arg1,Arg2)∈S ′′′d(Arg1 , Arg2) − d(Arg1, Arg2 )]+But

Figure 1: Figure shows relational paragraph embeddings.
We use a paragraph vector model to obtainthe feature for Arg1 and Arg2 (Le and Mikolov,
2014). The paragraph vector model is an idea toobtain a real-valued vector in the similar construction with the word vector model (or word2vec)(Mikolov et al., 2013b) where the detailed explanation can be obtained.
In implicit/explicit sense classification, the participated items related to this classification are
two for implicit relations of a pair (Arg1, Arg2)
and three for explicit relations of a triple(Arg1, Conn, Arg2).
This is by nature a
multiple-instance learning setting (Dietterich etal., 1997), which receives a set of instances which
5
6

www.psych.ualberta.ca/ westburylab.
www.statmt.org/wmt14.

where [x]+ denotes the positive part of x, γ > 0
is a margin hyperparameter. S ′ denotes a set ofcorrupted pair where Arg1 or Arg2 is replaced bya random entity (but not both at the same time).
Readers should see the detailed explanation in(Bordes et al., 2013).
It is noted that we tried indicator function (alternatively called discrete-valued vector, bucket
function (Bansal et al., 2014), binarization ofembeddings (Guo et al., 2014)) for embeddings
which are converted from real-valued vector. Although we have not tested sufficiently due to thetiming constraint, we did not include this methodin our experiments since we could not have any
gain.
2.2 Production Features for Constituent
Parsing(Lin et al., 2014) describes the method using the
production features based on the parsing results.

Subtree extract
Exact match+-1 position
Combi 2 elem
Combi 3 elem
Combi 4 elem

extracted
16582 0.347
39096 0.817
43031 0.899
45102 0.943
45872 0.959

not extracted
31265 0.653
8751 0.183
4816 0.101
2745 0.057
1975 0.041

Table 2: Extraction of production features for constituent parsing results.

In this paper, we further process and treat these as
the phrase embeddings. The algorithm is as follows. First, the subset of (constituent) parsing results which correspond to Arg1 and Arg2 are extracted. Then, all the production rules for thesesubtrees are derived. Third, we apply these production rules into the relational phrase embedding
model that we described in 2.1. We replace all thewords in 2.1 with production rules.

Figure 3: Figure shows the variation of the threshold in top X of word-pairs in each category. Mostof the frequent word-pairs are functional wordpairs, such as the-the, but we did not remove them.
sense classification
Contingency, Comparison, Temporal

country−it country−doesn easy−takeway−muchword−pair embeddings

2.3 Word-Pair Features

X

itword vectors

itWord-pair features in discourse parsing indicate
the Cartesian products of all the combinations ofwords in Arg1 and Arg2. This feature is usedin (Lin et al., 2014; Rutherford and Xue, 2015).
(Rutherford and Xue, 2015) further developed thismethod combined with Brown clustering (Brown
et al., 1992). We use this by word-pair embedding.
The second type of embedding we used in thispaper is an abstraction of word-pair embedding in
Arg1, Arg2 (and Conn) in a horizontal way. Thisis shown in Figure 4. The word grows their bigram in terms of Cartesian product of elements indifferent Arg1 and Arg2 which has a order from
Arg1 to Arg2 where this bi-gram is embeddedin the word embedding. Followed by Pitler et al.
(Pitler et al., 2008) we use the 100 frequent wordpairs in training set for each category of relation.
We did not delete function words/stop-words.
2.4 Heuristic Features for Explicit
Connectives
Heuristic features in this paper indicate the specific features used in the explicit sense classification: (1) connective, (2) POS of connective, and(3) connective + previous word (Lin et al., 2014).
These three features are employed in order to resolve the ambiguity in discourse connectives, andpractically work fairly efficiently.

itword vectors

word vectors

paragraphcountry

word vectors

word vectorsID
word vectors

word vectors

paragraphcountry

country−it
word−pair

word vectors

word vectors

ID

country−it
paragraphcountry

word−pairID

country−it
word−paircountry−doesn
country−itfunds−itoffer−itan−itcountry
funds
offer
an

to
much
take

easy
way

Arg1

’t
doesn

Arg2

it

Figure 4: Figure show word-pair embeddings.

3 Experimental Settings
For the dataset, we used the CoNLL 2015 Sharedtask data set, i.e. LDC2015E21 (Xue et al., 2015)
and Skip-gram neural word embeddings (Mikolov
et al., 2013a)7 ). For the unofficial run, we used
westbury version of English wikipedia dump (suchas Figure 2) and WMT14 data set.8
We choose python as the language to develop
our discourse parser. We use external tools suchas libSVM (Chang and Lin, 2011), liblinear (Fanet al., 2008), wapiti (Lavergne et al., 2010), andmaximum entropy model9 for a classification task
described as Section 2. Among these off-the-shelfclassifiers, we used libSVM for the official re7

https://code.google.com/p/word2vec
www.statmt.org/wmt14.
9
http://homepages.inf.ed.ac.uk/lzhang10/maxent.html
8

dev
f1 pr
Arg12
Arg1
Arg2
conn
parser
sense
Arg12
Arg1
Arg2
conn
parser
sense
Arg12
Arg1
Arg2
conn
parsersense

Overall Task
test
f1 pr rec

blind
rec
f1 pr recOverall.291 .250 .348 .246 .210 .297 .215 .188 .252
.392 .336 .469 .357 .304 .431 .317 .276 .371
.422 .362 .505 .398 .339 .480 .382 .333 .448
.863 .904 .827 .881 .903 .859 .794 .849 .746
.154 .132 .184 .123 .105 .149 .107 .093 .125
.081 .270 .099 .083 .207 .112 .041 .047 .065
Explicit Only
.186 .195 .178 .147 .150 .143 .111 .119 .104
.263 .275 .252 .211 .216 .206 .167 .178 .157
.373 .391 .357 .382 .392 .373 .281 .301 .264
.863 .904 .827 .881 .903 .859 .794 .849 .746
.158 .166 .152 .132 .136 .129 .079 .084 .074
.138 .263 .142 .108 .175 .110 .077 .077 .084
Implicit Only
.355 .275 .501 .307 .237 .436 .276 .217 .378
.453 .351 .640 .430 .332 .610 .392 .309 .538
.451 .349 .638 .407 .314 .578 .441 .347 .603
1
1
1
1
1
1
1
1
1
.151 .117 .213 .117 .091 .166 .123 .097 .169.019 .699 .052 .025 .667 .061 .016 .598 .046

Sense Classification
dev
test
f1 pr rec f1 pr

rec

1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.492 .812 .474 .466 .804 .458
.546 .546 .546 .531 .531 .531
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.707 .882 .694 .727 .726 .838
.838 .838 .838 .873 .873 .873
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
1
.283 .283 .283 .221 .221 .221
.105 .803 .136 .112 .891 .149

Table 3: Official results for task of identification of connectives and arguments. Table shows the resultsfor dev set, test set and blind test set.
sults. Additionally we use word2vec (Mikolov etal., 2013b) and Theano (Bastien et al., 2012)10 in
the pipeline.
One bottleneck of our system was in a training
procedure. Since a paragraph vector is currently
not incrementally trainable, we were not able toseparate training and test phases. Hence, we needto run it all on TIRA,11 whose computing resourceis powerless which took a considerable time suchas 15 to 30 minutes where most of other participants only finish their run in 30 seconds or so.

4

Experimental Results

Table 3 shows our results. There are fifteen
columns where the nine columns in the left show
the overall task while the six columns in the rightshows the supplementary task.12
In terms of the evaluation for explicit connectives, we obtained F score of 0.138, 0.108, and10

http://deeplearning.net/tutorial/rnnslu.html
http://www.tira.io
12
Due to the unforeseen errors occurred on TIRA, we
could not obtain the results for blind test set.
11

0.077 for dev/test/blind sets for overall task (thelowest low in the second group) while we obtained F score of 0.707 for sense classification
task. For the connectives, F score was 0.863
while Arg 1-2 was 0.186 which was fairly low.
This may be result in the policy of the evaluation script which checks the correct classification results together with the correct identificationof triples (Arg1, Conn, Arg2). Hence, even if
the classification results were correct if the triples(Arg1, Conn, Arg2) were not correctly identified, the results were not correct. Thus, this explains why there is a big difference between theoverall task (left nine columns) and the sense classification task (right three columns), as well as thelow scores of 0.138, 0.108 and 0.077.
For the implicit only evaluation, on contrast, we
obtained F score of 0.019, 0.025, and 0.016 (thelowest row in the third group) for overall task and0.105 for sense classification task. Here, precision
was high (precision of these which were 0.699,
0.667, and 0.598) for overall task and 0.803 and0.891 for sense classification task; while recall

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

dev set (official results)
Explicit
Implicitpr
rec f1
pr
rec
Comp
1
0
0
1
0
Comp.Conc.13 .17 .14 1
0Comp.Cont.79 .84 .82 1
0Cont.Cau.Rea .96 .58 .72 1
0Cont.Cau.Res 1.84 .91 1
0
EntRel–––.28 .95
Exp
–
–
–
1
0
Cont.Cond1.89 .94 ––Exp.Alt
.86 1
.92 1
0
Exp.Alt.C alt 1.83 .91 1
0
Exp.Conj.96 .96 .96 .26 .04
Exp.Inst
.90 1
.95 0
0
Exp.Rest
1.33 .50 .50 .04
Temp.As.Pr.94 .96 .95 10
Temp.As.Su.95 .73 .82 1
0
Temp.Syn.62 .97 .76 1
0
Average.88 .69 .71 .80 .14
Overall.84 .84 .84 .28 .28

f1
0
0
0
0
0
.44
0
–
0
0
.07
0
.07
0
0
0
.11
.28

dev set (unofficial results)
Implicit(30m) Implicit (prod) Implicit (wp)pr
rec f1pr
rec f1pr
rec f1
1
0
0
1
0
0
1
0
0
1
0
1
0
0
0
0
0
0
1
0
0
.05 .02 .03 .16 .2
.18
1
0
0
.09 .10 .03 .24 .10 .14
10
0.08 .08 .08 .15 .08 .10.29 .98 .45 .24 .91 .43 .36 .69 .47
1
0
0
1
0
0
1
0
0
–
–
–
–
–
–
–
–
–
1
0
0
1
0
0
1
0
0
1
0
0
0
0
0
1
0
0
.54 .11 .18 .17 .15 .16 .35 .26 .30.75 .06 .12 .09 .23 .13 .43 .06 .11.33 .01 .02 .14 .16 .15 .11 .06 .08
10
0.13 .04 .06 0
0
0
1
0
0
0
0
0
1
0
0
1
0
0
.08 .10 .09 000.86 .14 .11 .21 .14 .07 .39 .16 .09.30 .30 .30 .16 .16 .16 .29 .29 .29

Table 4: Results for devset (Official and unofficial results). Implicit only includes Implicit, EntRel, and
AltLex. This experiment uses the development set. The right most column Implicit only(30m) shows theresults with additional data of 30M sentence pairs using the same setting of Figure 2.
was very low.
Table 4 and 5 shows the detailed results forsense classification under the setting that identification of connectives and arguments are correct. The first group (the left three columns) show
the results for explicit classification. On contrast to implicit classification all the figures are
considerably good except Comp.Conc whose Fscore was 0.14. The second group to the fifthgroup (the rightmost three columns) show four
configurations of implicit classification. The thirdgroup shows the 30 million additional sentencepairs for training, the fourth group uses production feature, and the fifth group uses word-pair feature. These three groups exposed each characteristics quite clearly. Relational phrase embeddings(Implicit and Implicit(30m)) works for Expansion
group (Exp.Conj, Exp.Inst, Exp.Rest), the production feature (marked as Implicit(prod)) worked for
Temporal group (Temp.As.Pr and TempSyn), andthe word-pair feature (marked as Implicit(wp))
worked for Comparison/Contingency groups. The
effect of additional data was shown in the thirdgroup (marked as Implicit(30m)). This group was
given additional data of 30M sentence pairs which

improved the performance on Exp.Conj (from F
score 0.07 to 0.18), and Exp.Inst (from F score0.00 to 0.12) while Exp.Rest was down from fiscore 0.07 to 0.02. The effect was limited to thesecategories.
It is easily observed that if the surface form ofconnective does not share multiple senses, suchas if (67%) in Cont.Cond and instead (87%) in
Exp.Alt.C, the results of sense classification performed good where Cont.Cond was F score of 0.94
and Exp.Alt.C was F score of 0.91. If the surface
form of connective share multiple senses, theytend to be classified unbalancedly and one sense
tends to be collected many votes. (For example,
But has multiple senses, including Comp.Conc,
Comp, and Comp.Cont. Comp.Cont collectedmany votes. As a result, the classification resultsfor Comp.Cont was good but for others they werebad).

5 Discussion
A paragraph vector is proven useful for the sentiment analysis-typed task (Le and Mikolov, 2014).
The word embedding is propagated towards theparent node and averaged. Our intension was that

1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18

test set
Explicitprrec1
0.41 .59.91 .83
1
.75
1
.97
–
–
–
–
1
.81
.83 1
1
1
.98 .98
1
1
1
.29
.92 1
.94 .69.58 .98.84 .73.87 .87

f1
0
.48
.87
.86
.99
–
–
.89
.91
1
.98
1
.44
.96
.79
.73
.73
.87

Implicit
pr
rec
–
–
1
0
1
0
1
0
1
0
.22 .96
1
0
–
–
–
–
1
0
.25 .09
1
.04
1
0
1
0
1
0
1
0
.89 .15.22 .22

f1
–
0
0
0
0
.35
0
–
–
0
.13
.08
0
0
0
0
.11
.22

Table 5: Official results for explicit/implicit senseclassification for test set.

the averaged embedding in a sentence will perform meaning establishment in the intermediate
representation which capture the characteristics of
Arg1, Arg2, and Conn. First, Comp.Cont orComp.Conc may include sentence polarity withsome additional condition that these polarities
may be reversed. Against our expectation only ahandful of examples were classified in these categories. However, if they are classified in thesecategories they were correct, i.e. precision 1.
Second, if Arg1 and Arg2 are required to expose the causal relation such as Cont.Cau.Rea
and Cont.Cau.Res this may be beyond the framework of a paragraph vector. Third, our implicit classification tried to classify Exp.Conj and
Exp.Rest. Both of these categories of relation
can be found some similarities with sentiment
analysis/polarities, which can be reasonable that
it worked for these categories. Four, interestingly, the word-pair feature works for Comparison/Contingency sense group while the production
feature works (only slightly though) for Temporalsense group.
We used a margin-based ranking criteria to obtain relations over a paragraph vectors. First,(Mikolov et al., 2013b) observed a linear relation

on two word embeddings. However, it might be
too heavy expectation for two paragraph embeddings which can capture the similar phenomenon.
Even if Arg1 consists of many words, a paragraphvector will average their word embeddings. In thissense this approach may have a crucial limit together with the fact that this is unsupervised learning. Second, we do not know yet but some small
trick may improve the relation of Comp.Cont orComp.Conc since these relations are quite similar relations with Exp.Conj, Exp.Instantiation, andExp.Rest except that these relations are the polarities reversed.

6 Conclusion
This paper describes the discourse parsing systemdeveloped at Dublin City University for participation in the CoNLL 2015 shared task. We take anapproach based on a paragraph vector. One shortcoming was that our classifier was effective only
Exp.Conj, Exp.Inst and Exp.Rest despite our expectation that this model will work for Comp.Cont
and Comp.Conc as well. The relation of the latteris in an opposite direction. We provided the wordpair model which works for these categories but ina different perspective.
Further work includes the mechanism how tomake it work for Comp.Cont and Comp.Conc.
Although a paragraph vector did not work efficiently, our model has a tentative model which
does not have interaction between relational, paragraph, and word embeddings such as in (Denilet al., 2015), which is one immediate challenge.
Then, other challenge includes replacement of aparagraph vector model with a convolutional sentence vector model (Kalchbrenner et al., 2014) and
RNN-LSTM model (Le and Zuidema, 2015). The
former approach is related to the supervised learning instead of unsupervised learning. The latterapproach is to employ the structure of tree insteadof a sequence.

Acknowledgment
This research is supported by the Science Foundation Ireland (Grant 12/CE/I2267) as part of the
ADAPT at Dublin City University. We would also
like to thank ICHEC, the CoNLL 2015 Shared
Task Committee, and Martin Potthast.

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