8th International Symposium on
Applied Engineering and Sciences (SAES2020)
Virtual Conference / 12th–19th December 2020

Relation Identification Using Dialogical Features in Multi-Party
Conversation
Takumi Himeno1 * and Kazutaka Shimada1
1

Department of Artificial Intelligence, Kyushu Institute of Technology, 680-4 Kawazu, Iizuka,
Fukuoka 820-8502, JAPAN* Corresponding author’s e-mail: t_himeno@pluto.ai.kyutech.ac.jp
Keywords: Multi-party Conversation, Argument Mining, Argument Structure

Introduction: To understand the content of a discussion in meetings, a summary is important forpeople who did not attend the discussion. If the summary is illustrated as a discussion structure, it
is helpful to grasp the essentials of the discussion immediately. In this paper, we construct models
that predict a link between nodes that consist of some utterances in multi-party conversation.
Various neural network models have been proposed for many tasks. However, those neuralnetwork models can usually capture only the linguistic feature. On the other hand, dialogicalfeatures are useful for understanding conversations. Therefore, we introduce some dialogicalfeatures to our method.
Approach: We utilize three models based on machine learning for the prediction of a link between
nodes, namely SVMs, Bi-LSTM and BERT. The basic feature for the three models is based on
linguistic features; BOW for SVMs and Word2Vec for the three models. We assume that a modelwith dialogical features leads to the improvement of the accuracy as compared with the models
with only linguistic features, for the multi-party conversation settings. Therefore, we create eighttypes of dialogical features such as time information, for the three models. In this paper, we use
219 discussion from 92 dialogs of the AMI corpus [1]. We divide the AMI corpus into 201
discussions from 84 dialogs for the training data, 13 discussions from 4 dialogs for thedevelopment data, and 12 discussions from 4 dialogs for the evaluation data. However, theexperimental data are imbalanced. The linked pairs are just 3850 as against 38530 not-linked pairs.
Models generated from imbalanced data usually become a weak classifier. Therefore, we
randomly select 3850 not-linked pairs from the original training data for all models. Then, we
generate each model from the downsized and balanced training data.
Results and Discussion: We compared the effectiveness of our proposed features. Table 1 andTable 2 show the experimental results with our features and that without our features, respectively.
All the F-scores of the models with our features were improved as compared with those without our
features. This result shows the effectiveness of our dialogical features. By applying our dialogicalfeatures, our model dramatically boosted all scores about not-linked pairs. Next, we analyzed
errors in the experimental results. One typical error was nodes with back-channel feedback. The
back-channel nodes were often incorrectly connected with nodes about questions. Nodesconsisting of some words or short phrases, such as back-channel feedback, are ambiguous, andthey tended to connect with all sorts of nodes. Creating new dialogical features to capture thisproblem is the most important future work.

Table 1: Result of methods with our proposed
Table 2: Result of methods without proposedfeature.
feature.
[1] Carletta, Jean, et al. "The AMI meeting corpus: A pre-announcement." International workshop on
machine learning for multimodal interaction. Springer, Berlin, Heidelberg, 2005.

Relation Identification Using Dialogical Featuresin Multi-Party Conversation
Takumi Himeno and Kazutaka Shimada
Department of Artificial Intelligence
Kyushu Institute of Technology
Fukuoka, Japan{t himeno, shimada}@pluto.ai.kyutech.ac.jp
Abstract
To grasp the content of a discussion in meetings, a summary is important for people who could
not attend the discussion. If the summary is illustrated as a discussion structure, it is helpful tograsp the essentials of the discussion immediately. In this paper, we construct models that predicta link between nodes that consist of some utterances in multi-party conversation. Various neuralnetwork models have been proposed for many tasks. However, those neural network models canusually capture only the linguistic feature. On the other hand, dialogical features are useful formodels to predict a link between nodes. First, we explain the features that we design for the task.
Next, we report the result in which we compared a machine learning method using the proposedfeature with one without them. The result shows that time information and distance between
nodes were effective.

1

Introduction

Meetings are often held in laboratories and companies to come up with new research ideas and management strategies. To grasp the content of a discussion, a summary is important for people who could
not attend the discussion. A summary is suitable for understanding the main discussion points. Assume
that a summary is illustrated as a discussion structure. The summary is more powerful and helpful tounderstand the main discussion points because users can immediately capture the flow of the discussion
by using links between utterances For the purpose, we need to estimate the discussion structure fromeach discussion.
Argument mining is one of the tasks to construct a structure of sentences (Stab and Gurevych, 2017a).
It automatically derives the structure of argumentation from unstructured documents such as essays. It
consists of the four subtasks as follows: component identification, component classification, relation
identification, and relation classification. Component identification is the task that extracts argumentcomponents from a given document. Argument components denote sentences and paragraphs that related
to the discussion structure. Component classification is the task that assigns a label, e.g., claim, to eachargument component. Relation identification is the task that predicts whether each pair of argumentcomponents is related or not. Relation classification is the task that assigns a label, such as “attack” and“support”, to the related pairs of argument components. In this paper, we focus on relation identification
for constructing the discussion structure in a multi-party conversation. In other words, we construct a
prediction model of links between nodes consisting of some utterances.
In recent years, neural network models are used in many tasks of natural language processing. In
argument mining, some researchers have proposed models based on neural networks (Deguchi and Yamaguchi, 2019; Qin et al., 2017). We also apply neural models to our task. However, these neural networkmodels tend to use only linguistic features. On the other hand, dialogical features, such as time information, are useful for models to predict a link between nodes. Therefore, we introduce some dialogicalfeatures to our method.
The contributions of this paper are as follows: (1) we show the effectiveness of our dialogical featuresin the relation identification task with the AMI corpus and (2) we compare SVMs, Bi-LSTM, and BERT
models with/without the dialogical features.

2

Related Work

Automatic summary generation is one of the most important studies to help people that want to understand the main discussion points easily. Mehdad et al. (2013) have proposed a method that automaticallygenerates a summary of the content of a discussion by extracting important utterances in the discussion.
Although a summary is suitable for grasping the content of the whole discussion, it is not always suitable
to understand the structure of discussion points immediately. In this paper, we focus on a prediction taskof links between utterances for grasping the structure of the discussion.
In recent years, argument mining is attracting attention in natural language processing. Argument mining is a task to construct the structure of a document. It is applied to many natural language processing
tasks such as document summarization (Barker and Gaizauskas, 2016; Peldszus, 2014), the automaticscoring of essays (Ghosh et al., 2016), the paper writing support (Stab and Gurevych, 2017b; Nguyen
and Litman, 2016), the information retrieval (Stab et al., 2018) and so on. Stab and Grevych (2014)
have tackled the relation identification for essays written by students. They created some features that
capture the characteristics of the essay and predicted the link between argument components. The essay is usually formalized, such as the form of a claim followed by premises. However, the multi-party
conversation is not formalized because many people freely speak to assert their opinions. Therefore,
we introduce some features that capture the dialogical characteristics, such as time information, for themulti-party conversation task.
As a method that predicts the link between argument components, Potash et al. (2017) have proposed amethod based on Pointer Network (Vinyals et al., 2015). They applied some features, namely BOW and
embedding, for the task. However, as mentioned above, not only linguistic features but also dialogicalfeatures are important for the multi-party conversation task. In this paper, we prepare some features tocapture the characteristics of the multi-party conversation: for example, speaker ID, time information,and distance between nodes.

3

Dataset and Task

3.1 Dataset
In this paper, we use the AMI corpus, a multi-party conversation corpus (Carletta et al., 2005). It contains
useful various annotations, such as the argument structure and time information, to predict a link between
nodes. Each node consists of one or more utterances. We use scenario meetings that are held with thediscussion points given in advance. In the discussion setting, four employees in different roles in acompany discuss developing a new TV remote control that replaces an old-style TV remote control for
consumers on the market. This discussion is held four times. Each utterance in the AMI corpus contains
speaker ID, time information, and a dialog act. A dialog act indicates what intention each utterance
represents. The details of the dialog act are shown in Table 1. This corpus is annotated with a total of
15 types of dialog act tags such as “Inform” representing an utterance providing some information and“Backchannel” giving responses in a word. In this paper, we introduce dialogical features by using thesetags.
In this paper, we use the annotated data1 based on the Twente argument schema (TAS) to contain thelink between nodes (Rienks et al., 2005). TAS is an annotation schema created to clarify the discussion
structure which arises from the scenario meeting of the AMI corpus. The discussion structure in TAS
consists of two elements. One is the node and the other is the edge. The node in TAS contains parts of,or even complete, speaker turns. The edge in TAS represents the type of relation between the nodes. In
TAS, the Unit Label that represents the role of the node is also annotated The details of the Unit Labelare shown in Table 2. In addition, TAS defines “discussion” as segments in the meeting (“Dialog”). One
dialog consists of one or more discussions, one discussion consists of some nodes and one unit label isassigned to each node.
1

http://groups.inf.ed.ac.uk/ami/download/Tag Type
Backchannel
Stall
Fragment
InformElicit-Inform

DetailUtterances such as giving the response
Utterances playing as the filled pauses
Utterances which do not convey a speaker intention
Utterances in which the speaker gives information to the listener
Utterances in which the speaker asks the listener for information
Utterances in which the speaker expresses an intention
about actions
Utterances offering the speaker’s own action
Utterances in which the speaker expresses a desire for someone
to make an offer or suggestion.
Utterances about any comment that expresses an evaluation
Utterances showing the speaker’s own understanding
Utterances in which the speaker attempts to elicit an assessment

SuggestOffer
Elicit-Offeror-suggestion
AssessComment-about-UnderstandElicit-Assessment
Elicit-Commentabout-Understanding

Utterances in which the speaker attempts to elicit a comment
Utterances which are intended to make an individualor the group happier
Utterances which express negative feelings towards an individualor the group
Utterances which don’t fit any of the other classes

Be-Positive
Be-Negative
Other

Table 1: Detail of dialog act tags in the AMI corpus.

3.2 Task
Figure 1 shows an example of the relation identification process. In Figure 1, the dialog contains two
discussions: discussion1 and discussion2. The two discussions contain three nodes and two nodes respectively. ♦ in each discussion denotes true links. For example, the pairs of node1-node2 and node1-node3
contain the link that we want to predict.
First, we extract all combinations of two nodes in each discussion. In Figure 1, three pairs are extracted
from discussion1 and one pair of node4 and node5 is extracted from discussion2. Next, our model
classifies each pair into linked or not-linked pairs. We evaluate whether the result corresponds to theground truth.

Dialog with true links

Node combinations

discussion1
node2
Label:
Statement

node3
Label:

node2

node1

node3

node2

node3

node4

node5

Weak Statement

discussion2
node4
Label:
A/B Issue

node5
Label:
Statement

LinkedLink prediction model

node1
Label:
A/B Issue

node1

Not-linked
Not-linked

LinkedFigure 1: Relation identification. We handle all combinations of node pairs in each discussion for the
relation identification task. The task is to classify each pair into “linked” or “not-linked”.

Tag TypeStatement
Weak Statement
Open Issue
A/B Issue
Yes/No Issue
Other

Detail
A claim without a weakening qualifier
A claim with a weakening qualifier
An issue that are raised where
every possible response could be a solution
An issue that are raised where
the possible responses are explicitly enumerated
An issue that are raised where
the possible responses are Yes and No
Not fitting any of the other Unit Labels

Table 2: Detail of the unit labels in the TAS.

4

Proposed Method

In this section, we explain three models based on machine learning for the prediction. First, in Section
4.1, we explain the models as follows; SVM, Bi-LSTM, and BERT. The basic feature for the three models
is based on linguistic features; BOW for SVMs and Word2Vec, namely word embedding, for the threemodels. Then, in Section 4.2, we explain our proposed features based on dialogical characteristics.
4.1 Basic models with linguistic features
4.1.1 SVM
We apply Support Vector Machines (SVMs) to the prediction task. Here SVMs handle two types offeature spaces; BOW features and features based on word embedding. As the BOW features, we use allwords without the stopword list by NLTK. As the word embedding, we use word2vec (W2V) 2 publishedby Google. We generate the vector space as follows:
Vnoden =

m
∑

vx

(1)

x=1

where vx denotes the word vectors of noden and m denotes the size of the node. For example, assume
that node1 and node2 consist of embedding (vx ) of words in each node. We obtain two summed wordembedding vectors, namely Vnode1 and Vnode2 . Finally, SVMs learn and predict the relation by using
concatenated Vnode1 and Vnode2j .
4.1.2 Bi-LSTM
The second model is based on the Bi-LSTM model to predict the relation (Hochreiter and Schmidhuber,
1997). Figure 2 shows an overview of the Bi-LSTM model. We use W2V in each node as the input forthe model. The W2Vs in nodei and nodej are learned by Bi-LSTM, respectively. The model concatenates Vnodei and Vnodej that are obtained from the Bi-LSTM. Finally, the softmax function produces theestimated probability of the relation from the dense layer3 .
4.1.3 BERTThe third model is based on BERT (Devlin et al., 2018). The BERT model transforms the input word
vector based on W2V in two nodes into feature vectors. We extract the 11th layer from the BERT since
the last layer tends to be too close to the target functions during pretraining. It is often biased toward thetarget. Finally, we compute the softmax function from the dense layer by the 11th layer4 .
2

http://code.google.com/archive/p/word2vec/
As mentioned in Figure 2, we concatenate the proposed features described in Section 4.2 to the dense layer if we provide
the option
4
In a similar to Bi-LSTM, We also concatenate the proposed features in the next section to the dense layer if we provide theproposed features.
3Output
Dense

Proposedfeatures

…

…

…

…

Figure 2: The Bi-LSTM and dense network. We concatenate our proposed features with the outputs of
LSTMs.

C

...

...

Layer12

C

...

...

Layer11

BERT

...

CLS

...

...

sep

OutputBERT feature vectors
Dense Layer
Layer1...

Proposed features

Figure 3: The BERT model. We use the 11th layer for integration with our features.
4.2 Proposed Features
A model with dialogical features leads to the improvement of the accuracy as compared with the models
with only linguistic features, namely models in Section 4.1, for the multi-party conversation settings. In
this section, we explain eight types of dialogical features for our model. For the model based on BiLSTM and BERT, Figure 2 and Figure 3 have already indicated the usage instructions of our proposedfeatures in this section. For the SVMs, we concatenate the proposed features in this section to the vector
space based on BOW or W2V.
• Number of words in node pair (NW)
If the speaker supports and attacks the other speaker’s claim, the size of the node tends to be larger5 .
In a similar way, the node also tends to be larger if the speaker wants to convey a lot of information
to the other speaker. On the other hand, the size of the node becomes smaller if the node consists ofshort utterances, such as back-channel feedback. Thus, the size of each node is one of the importantcharacteristics. To capture this feature, we use the number of words in each node.
• Number of common words in node pairs (NCW)
If two nodes are related to a common topic, words in them frequently are overlapped. Therefore,
we count the number of common words that appear in each node as the feature.
5

The size denotes the number of utterances in a node in this context.

• Speaker information (SI)
If a speaker claims own opinion, a speaker who gives a positive opinion and points out a problem
about the opinion differs from him/her. Besides, the situation that the same speaker gives a positive
opinion to own claim or point out a problem of his/her claim is very rare. Therefore, speaker
information of each node has an important role in the relation between two nodes. We use thespeaker ID of each node as the feature.
• Time information (TI)
If the discussion is active, the time interval between nodes tends to become shorter. Links between
a node in the early stage and a node in the last stage in a discussion is rare. In other words, far-flungnodes usually do not possess a link. To capture this feature, we focus on time information in the
corpus. We compute the time information by using the end time of a node and the start time ofanother node as the feature.
• Distance between nodes (DN)
Assume that the discussion is stagnant. In this situation, the distance between nodes becomes shortbecause the number of nodes in the stagnant situation becomes small6 . Thus, the distance, namely
the number of nodes that appear between two nodes, is one important feature. Therefore, we sort thenodes in a discussion in terms of the timestamps and use the distance between nodes as the feature.
• Dialog act (DA)
Dialog act tags are important information for the prediction model. For example, if a node contains
the Inform tag, the node tends to connect with nodes that contain “Backchannel” and “Assess”
because of the nature of discussions. On the other hand, a node with the Elicit-inform tag does not
usually connect with the Inform tag because the Elicit-inform tag is used by a speaker to request thatsomeone else give some information while the Inform tag is used by a speaker to give information.
Therefore, we use the distribution of 15 types of dialog acts in each node as the feature.
• Unit label (UL)The unit labels described in Section 3 also have an important role in the prediction of the link
between nodes. They contain three types of labels that are related to questions; “Open Issue”, “A/B
Issue”, and “Yes/No Issue”. In the situation that a node contains such tags, the node tends to connect
with nodes that express positive/negative opinions. Besides, nodes with such tags do not generally
connect with nodes about questions because it is a question-question pair. Therefore, we use theunit label of each node as the feature.
• Polarity of node pair (P)Emotional information is also one of the characteristics of conversations. For example, a speaker
may emotionally argue while claiming his/her opinion in a discussion. In a similar way, when aspeaker may emotionally argue when he/she agrees or disagrees with another speaker’s question.
To capture the information, we use Stanford CoreNLP (Manning et al., 2014). We compute thescore (1 to 5) of each utterance by using CoreNLP. Then we compute the average score from thescore of the utterances in each node. We use the average polarity score of each node as the feature.

5

Experiment and Analysis

In this section, we describe the experimental setup of the three models in Section 4.1. Next, we describe
the experimental data. Finally, we report the experimental result and discuss the influence of the proposedfeatures.
6
Note that this distance denotes the number of lines when each utterance is transcribed by one line. This is essentiallydifferent from the time information feature.

Training data
Development data
Evaluation data

Dialog8444Discussion
201
13
12

Linked pair
3850
235
238

Not-linked pair
38530
1822
1875

Table 3: Distribution of the experimental data. For the training data, we select 3850 not-linked pairs
randomly to generate balanced training data.

SV MW 2V
SV MBOW
Bi-LST M
BERT

Linked pair
Precision Recall F-score
0.41†
0.84†
0.56†
†
†
0.39
0.89
0.55†
0.42†
0.46
0.44†
†
†
0.27
0.90
0.41†

Not-linked pair
Precision Recall F-score
0.98†
0.85†
0.91†
†
†
0.98
0.82
0.90†
0.93†
0.92†
0.92†
†
†
0.98
0.69
0.81†

Table 4: Result of methods with our proposed feature. Each † denotes that the score is better than that of
the baseline in Table 5, namely the effectiveness of our method.

5.1 Experimental Setup
For the SVM model, we use LiBSVM (scikit-learn) for the implementation (Chang and Lin, 2011). The
kernel function was the RBF function and the cost parameter was 100.
For the Bi-LSTM, the input layer dimensions were set to 300. The batch size was 16. NLLoss wasused as the loss function. The optimizer was Adam (Kingma and Ba, 2015); the learning rate was 0.001
and the drop out was 0.2. When the Bi-LSTM model learned the training data, the Bi-LSTM model
evaluated the development data for each epoch at the same time. The smallest value of the loss functionin the development data appeared in 25 epochs. Therefore, we used the model as the final model.
For the BERT model, we used the BERT-Base as the pre-trained model. The text has been lowercased.
The batch size was 16. We used cross-entropy as the loss function. The optimizer was Adam and thelearning rate was 0.00002.
5.2 Experimental Data
We used 219 discussions from 92 dialogs of the AMI corpus. In this experiment, all nodes in each
discussion were given and we used oracle unit labels in the corpus for the feature extraction. We divided
the AMI corpus into 201 discussions from 84 dialogs for the training data, 13 discussions from 4 dialogs
for the development data, and 12 discussions from 4 dialogs for the evaluation data. As explained in
Section 3.2, we generated all combinations of two nodes in each discussion. The distribution, such as
the number of linked pairs and not-linked pairs, was shown in Table 3.
Table 3 said that the experimental data were imbalanced; the linked pairs were just 3850 as against
38530 not-linked pairs. Models generated from imbalanced data usually become a weak classifier. Therefore, we reduced the imbalance of the training data. For all models, we randomly selected 3850 notlinked pairs from the original training data. Then, we generated each model, namely SVMs, Bi-LSTM,

SV MW 2V
SV MBOW
Bi-LST M
BERT

Linked pair
Precision Recall F-score
0.12
0.55
0.20
0.13
0.53
0.22
0.15
0.55
0.24
0.16
0.60
0.25

Not-linked pair
Precision Recall F-score
0.90
0.49
0.64
0.91
0.57
0.70
0.91
0.61
0.73
0.92
0.60
0.73

Table 5: Result of methods without our proposed feature.

Proposed Feature
ALL
−NW
−NCW
−SI
−TI
−DN
−DA
−UL
−P

Precision
0.41
0.40
0.41
0.40
0.30
0.38
0.41
0.41
0.40

Recall
0.84
0.86
0.84
0.84
0.84
0.85
0.84
0.84
0.84

F-score
0.56
0.55
0.55
0.54
0.44
0.52
0.55
0.55
0.55

Table 6: Ablation test. This result was based on the best model, namely SV MW 2V .

and BERT, from the downsized and balanced training data.
5.3 Experimental Results
We compared the effectiveness of our proposed features. Table 4 and Table 5 show the experimental
result with our features and that without our features, respectively. The boldface denotes the best scorefor each criterion, namely Precision, Recall, and F-score, in the table. The scores with † in Table 4
denote that the scores are better than those of the models without our features. All the F-scores of themodels with our features were improved as compared with those without our features. This result showsthe effectiveness of our dialogical features. By applying our dialogical features, our model dramatically
boosted all scores about not-linked pairs. The features contributed to reducing fallacious edges by thebasic models based on only the linguistic feature.
Recall rates of linked pairs of SV MBOW and BERT were higher than SV MW 2V although the best
F-score was produced by SV MW 2V . For this reason, W 2V lost information of specific words that wereeffective to the prediction due to the summation of word embeddings (Eq. (1)). On the other hand,
BOW and BERT can handle such information correctly.
However, the precision rates of the linked pairs were not sufficient as compared with other criteria.
The best score was just 0.41 on the SV MBOW model. Our models tended to become overly committed
to the linked pairs. In other words, our models generated too many edges between linked pair nodes.
On the other hand, there are some restrictions about true links; only one incoming arrow to a node ispermitted although some outward arrows are permitted. The optimization of outputs of our models isinteresting future work.
Next, we analyzed errors in the experimental result. One typical error was nodes with back-channel
feedback. The back-channel nodes often incorrectly connected with nodes about questions. Nodes consisting of some words or short phrases such as back-channel feedback are ambiguous and tend to connect
with all sorts of nodes. The improvement of this problem is the most important future work. Moreover,nodes with long utterances tended to be misrecognized. Although such nodes contained a lot of information, our models were not able to capture the characteristics. This leads to the problem of the relatively
low recall rate of SV MW 2V . Specific expressions contribute to solving this problem. For example,
adverbs and auxiliary verbs are often used to emphasize the claim. Some conjunctions are also used tosupport the explanation of the claim and assist the claim. Capturing such linguistic characteristics andsurface expressions by additional features for the prediction is still important future work.
5.4 Analysis of Feature
We evaluated our features by the ablation test. The model in this ablation test is SV MW 2V , the best
F-score.
Table 6 shows the experimental result. ALL denotes the model with all features; the same as the scoresin Table 4. “−” denotes the ablation; e.g., −NW denotes the proposed model without the NW (Numberof words in node pair) feature. If the score decreases, the feature is important. The most important featurewas the TI (Time Information) feature. The result was probably caused by a dialogical assumption thatparticipants tend to immediately utter additional opinions or show a reaction after a participant’s opinion.
The DN (Distance between nodes) was also relatively effective for the prediction. Although the concept of DN is similar to that of TI, they are complementary; TI is for active discussions and DN is forstagnant discussions. Such dialogical features that were not captured from words themselves, namely
linguistic features, were important for argument mining on multi-party conversations.

6

Conclusions

In this paper, we proposed three models with additional features for the relation identification task onargument mining. Our target was the multi-party conversation corpus. We applied eight dialogicalfeatures into SVMs, Bi-LSTM, and BERT models. We compared the models with our features and those
without our features. Our models outperformed the basic model that did not handle our features. The bestmodel was SVMs with W2V and our features. We obtained the high recall rates for linked pairs although
the precision rates were insufficient. The improvement of the precision rate by additional features andthe optimization of the model’s output is important future work.
We also evaluated our features by the ablation test. The best feature was the time information featureand the second one was the distance feature. These features are complementary for active discussions
and stagnant discussions. These results show the effectiveness of our proposed methods.

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