D - REX : Dialogue Relation Extraction with Explanations
Abstract
Existing research studies on cross-sentence relation extraction in long-form multi-party conversations aim to improve relation extraction without considering the explainability of such methods.
This work addresses that gap by focusing on extracting explanations that indicate that a relation exists while using only partially labeled explanations.
We propose our modelagnostic framework, D - REX , a policy-guided semi-supervised algorithm that optimizes for explanation quality and relation extraction simultaneously.
We frame relation extraction as a re-ranking task and include relation- and entityspecific explanations as an intermediate step of the inference process.
We find that human annotators are 4.2 times more likely to prefer
D-REX’s explanations over a joint relation extraction and explanation model.
Finally, our evaluations show that D - REX is simple yet effective and improves relation extraction performance of strong baseline models by 1.2-4.7%.
Introduction
Traditional relation extraction (RE) approaches discover relations that exist between entities within a single sentence.
Recently, several approaches have been proposed which focus on cross-sentence
RE, the task of extracting relations between entities that appear in separate sentences (Peng et al., 2017; Quirk and Poon, 2017; Han and Wang, 2020;
Yao et al., 2019) as well as cross-sentence RE in dialogues (Yu et al., 2020; Chen et al., 2020; Xue et al., 2021; Qiu et al., 2021; Lee and Choi, 2021).
A crucial step towards performing crosssentence RE in multi-entity and multi-relation dialogues is to understand the context surrounding relations and entities (e.g., who said what, and to whom).
Figure 1 shows an example from the DialogRE dataset where a simple BERT-based model (Initial Predicted Relation in Figure 1) gets confused by multiple entities and relations existing in the same dialogue (Yu et al., 2020).
The model predicts the “girl/boyfriend” relation between Speaker 2 and Chandler, however, it is clear from the context that the “girl/boyfriend” relation is referring to a different pair of entities: Speaker 1 and Chandler.
One approach to encourage a model to learn the context surrounding a relation is by requiring the model to generate an explanation along with the relation (Camburu et al., 2018).
In addition to the
DialogRE dataset, Yu et al.
(2020) introduces manually annotated trigger words which they show play a critical role in dialogue-based RE.
They define trigger words as “the smallest span of contiguous text which clearly indicates the existence of the given relation”.
In the context of RE, these trigger words can be used as potential explanations.
Our work aims to extract explanations that clearly indicate a relation while also benefiting an
RE model by providing cross-sentence reasoning.
Our proposed approach, D - REX , makes use of multiple learning signals to train an explanation extraction model.
First, D - REX utilizes trigger words as a partial supervision signal.
Additionally, we pro- pose multiple reward functions used with a policy gradient, allowing the model to explore the explanation space and find explanations that benefit the re-ranking model.
Including these reward functions allows D - REX to learn meaningful explanations on data with less than 40% supervised triggers.
In order to predict relation- and entity-specific explanations in D - REX , we pose RE as a relation re-ranking task with explanation extraction as an intermediate step and show that this is not possible for a model trained to perform both tasks jointly.
Our contributions are summarized as follows:
• We propose D - REX ,
D ialogue R elation E xtraction with e X planations, a novel system trained by policy gradient and semisupervision.
• We show that D - REX outperforms a strong baseline in explanation quality, with human evaluators preferring D - REX explanations over 90% of the time.
• We demonstrate that by conditioning on DREX extracted explanations, relation extraction models can improve by 1.2-4.7%.
Problem Formulation
We follow the problem formulation of Yu et al.
: let d = ( s 1 : u 1 , s 2 : u 2 , . . .
, s n : u n ) be a dialogue where s i and u
i denote the speaker ID and the utterance from the
i th turn, respectively.
Let
E , R be the set of all entities in the dialogue and the set of all possible relations between entities, respectively.
Each dialogue is associated with m relational triples < s, r, o > where s, o ∈ E are subject and object entities in the given dialogue and r ∈ R is a relation held between the s and o .
Each relational triple may or may not be associated with a trigger t .
It is important to note that there is no restriction on the number of relations held between an entity pair; however, there is at most one trigger associated with a relational triple.
In this work, we consider an explanation to be of high quality if it strongly indicates that a relation holds, and for this purpose we consider triggers to be short explanations, though not always optimal in quality.
2. Relation Extraction (RE)
Given a dialogue d , subject s , and object o , the goal of RE is to predict the relation(s) that hold between s and o .
We also consider RE with additional evidence in the form of a trigger or predicted explanation.
Formally, this is the same as relation extraction with an additional explanation, ex .
2. Explanation Extraction (EE)
We formulate EE as a span prediction problem.
Given a dialogue d consisting of n tokens Tthrough T n , and a relational triple < s, r, o >, the goal of EE is to predict start and end positions, i, j in the dialogue, such that the explanation ex =
[ T i , T i +1 , . . .
, T j ] indicates that r holds between s and o .
Baseline Models
We first introduce approaches for RE and EE based on state-of-the-art language models.
We then propose a multitask approach that performs both tasks jointly.
Our approaches use BERT base (Devlin et al., 2019) and RoBERTa base (Liu et al., 2019b) pretrained models 2 , and follow their respective finetuning protocols.
For all models, we maintain a single input format, which follows from Yu et al..
Formally, for a dialogue d , subject s , object o , relation r , and explanation ex , the input sequence to all models is [CLS]{ r/ex [SEP]} s [SEP]
o [SEP] d , where { r/ex [SEP]} denotes that the relation or explanation may be included depending on the task setting.
For RoBERTa models, we use the 
 and 
 tokens rather than [CLS] and [SEP], respectively.
3. Relation Extraction (RE)
We follow the fine-tuning protocols of Devlin et al.
and Liu et al.
for BERT and RoBERTa classification models by using the output corresponding to the first token C ∈ R H ([CLS] and 
, respectively) as a latent representation of the entire input and train a classification matrix W ∈ R K x H , where K is the number of relation types and H is the dimension of the output representations from the language model.
For each relation r i , the probability of r i holding between s and o in d is calculated as P i = sigmoid ( CW Ti ) .
We compute the standard cross-entropy loss for each relation as
[Equation]
where y i denotes whether relation i holds.
3. Explanation Extraction (EE)
For EE, we use the input described above, with a natural language phrasing of a relation appended to the beginning of the sequence.
For example, if r is "per:positive_impression", then we concatenate "person positive impression" to the beginning.
We follow the fine-tuning protocol of Devlin et al.
for span prediction.
We introduce start and end vectors, S, E ∈ R H .
If T i ∈
R H is the final hidden representation of token i , then we compute the probability of token i being the start of the predicted explanation as a dot product with the start vector, followed by a softmax over all words in the dialogue:
[Equation]
To predict the end token, we use the same formula and replace the start vector S with the end vector
E .
To compute the loss, we take the mean of the cross-entropy losses per token for the start and end vectors.
Formally, let | d | be the number of tokens in dialogue d , then
[Equation]
where y Si and y Ei are the start and end labels.
Because we want explanations extracted only from the dialogue, if the start or end token with largest loglikelihood occurs within the first l tokens, where l is the length of [CLS] r [SEP] s [SEP]
o
[SEP], then we consider there to be no predicted explanation.
3. Joint Relation and Explanation Model
The joint RE and EE model uses the standard input from § 3.
It utilizes a BERT or RoBERTa backbone, and has classification and span prediction layers identical to those in the RE and EE models.
Similarly, the loss is computed as the weighted sum of
RE and EE losses:
[Equation]
where α is an adjustable weight.
In practice, we find that α = 0 .
5 works best.
Flaw of the joint model
The disadvantage of the joint model is this: supposing that an entity pair has 2 relations, each explanation should be paired with a single relation.
However, by making predictions jointly, there is no guaranteed mapping from predicted explanations to predicted relations.
One method of solving this issue is to predict relations and explanations in separate steps.
It is possible to first predict relations and then condition the explanation prediction on each individual relation and conversely.
This idea forms the basis for D - REX .
D - REX
In this section, we introduce the D - REX system.
We begin by introducing the models which make up the system.
Next, we present the training and inference algorithms.
Finally, we discuss the optimization objectives for each model in the system.
4. Models
The D - REX framework requires three components: an initial relation ranking model, an explanation model, and a relation re-ranking model, shown in
Figure 2.
Initial Ranking Model ( R )
In our algorithm and discussions, we use R to denote the initial ranking model.
There are no restrictions on R , it can be any algorithm which ranks relations (e.g., deep neural network, rule-based, etc.)
such as (Yu et al., 2020;
Lee and Choi, 2021).
However, if R needs to be trained, it must be done prior to D - REX training;
D - REX will not make any updates to R .
In our evaluations, we use the relation extraction model described in § 3.1.
The input to this model is ( s , o , d ) and the output is a ranking, R ( s, o, d ) .
Explanation Extraction Model ( EX )
In our algorithm and discussions, we use EX to denote the explanation model.
In this paper we limit our experiments to extractive explanation methods, as opposed to generative explanation methods, however this is not a limitation of D - REX .
The only limitation on the explanation model is that we require it to produce human-interpretable explanations.
Thus, it is also possible to use generative models such as GPT-2 (Radford et al., 2019) or graph-based methods such as (Yu and Ji, 2016; Xue et al., 2021) with adjustments to the formulation of the reward functions.
In our evaluations, we use the model as described in § 3.2.
The input to EX is ( r , s , o , d ) and the output is an extracted phrase from d , denoted as EX ( r, s, o, d ) .
Relation Re-Ranking Model ( RR )
In our algorithm and discussions, we let RR denote the relation re-ranking model.
In the D - REX training algorithm,
RR is updated through gradient-based optimization methods, and must be able to condition its ranking on explanations produced by EX .
In our experiments, we use the same model architecture as R and include an explanation as additional input to the model.
The input to RR is ( ex , s , o , d ) and the output is a relation ranking, denoted as RR ( ex, s, o, d ) .
Input: Pre-trained ranking, explanation, and re-ranking models: R , EX , RR k: for number of relations to re-rank Data: Dataset: D for ( s, r, o , t , d ) in D do
Compute ranking loss: L R RE ( s, o, d ) r pred ← R (s,o,d) 1: k for i in r pred do ex i ← EX ( r pred i , s, o, d ) Compute Re-ranking loss:
L RR RE ( ex i , s, o, d ) ; // Equation
Compute Re-Ranking Reward: R RR ; // Equation
Compute Leave-one-out Reward: R LOO ; // Equation
Compute policy gradient with rewards R
RR , R LOO ; // Equationendif t not empty then
Compute L EX ; // Equationend
Update EX , RR parameters with calculated losses end
4. D - REX Algorithm
The outline of this algorithm is shown in pseudocode in Algorithm 1.
Assuming that we have ranking, explanation, and re-ranking models R , EX , RR , then given a single datum ( s, r, o, t, d ), comprised of a subject, relation, object, trigger(may be empty), and dialogue, the D - REX algorithm operates as follows:
The ranking model takes as input ( s, o, d ) and computes the probability of each relation from the predefined relation types.
Next, we take the top-k ranked relations, r pred =
R ( s, o, d ) 1: k , and compute explanations.
For i = 1 , ..., k , explanations are computed as ex i = EX ( r pred i , s, o, d ) .
Finally, for each predicted explanation, the re-ranking model computes k probabilities for each relation type, using ( ex i , s, o, d ) as the input to RR .
The final probabilities for each relation type are computed as the mean across all k +1 predictions from R and RR .
4. Model optimization
We propose multiple optimization objectives to train an EX model that extracts explanations meaningful to humans and beneficial to the relation extraction performance while ensuring that
RR maintains high-quality predictions.
Explanation Model Optimization
We train
EX with supervision on labeled samples, and a policy gradient for both labeled and unlabeled samples, allowing for semi-supervision.
For the policy gradi- ent, we introduce two reward functions: a relation re-ranking reward and a leave-one-out reward.
Re-ranking Reward The purpose of the reranking reward is to ensure that EX predicts explanations which benefit RR .
Formally, let
L R RE ( s, o, d ) be the loss for R , given the subject, object, and dialogue: s, o, d .
And let L RR RE ( ex, s, o, d ) be the loss of RR , given the explanation, subject, object, and dialogue: ex, s, o, d .
Then we define the relation re-ranking reward as:
[Equation]
Because R is stationary, EX maximizes this function by minimizing L RR RE .
Of course, EX can only minimize L RR RE through its predicted explanations.
Leave-one-out Reward
The purpose of the leave-one-out reward is to direct EX in finding phrases which are essential to correctly classifying the relation between an entity-pair.
This reward function is inspired by previous works which make use of the leave-one-out idea for various explanation purposes (Shahbazi et al., 2020; Li et al., 2016).
We can calculate the leave-one-out reward using either R or RR , and it is calculated by finding the difference between the standard relation extraction loss and the loss when an explanation has been masked.
Formally, if d is the original dialogue and ex is the given explanation, let d mask ( ex ) be the dialogue with ex replaced by mask tokens.
Then, the leave-one-out reward is defined as:
[Equation]
Because L RE is calculated using the same model for both the masked and unmasked loss,
EX maximizes this reward function by maximizing the masked loss.
Of course, the only interaction that EX has with the masked loss is through the explanation it predicts.
Policy Gradient We view EX as an agent whose action space is the set of all continuous spans from the dialogue.
In this view, the agent interacts with the environment by selecting two tokens, a start and end token and receives feedback in the form of the previously discussed reward functions.
Let i, j be the start and end indices that the explanation model selects and T i be the i th token, then ex
= d [ i : j ] =
[ T i , T i +1 , . . .
, T j ] and the probabilities of i, j being predicted are calculated as P ST i and
P ET j according to equation 2.
For both reward functions, we use a policy gradient (Sutton and Barto, 2018) to update the weights of the explanation model and calculate the loss as
[Equation]
Additionally, while training EX in the DREX algorithm, we make use of supervision when available.
In the case where supervision exists, we calculate an additional loss, L EX , as defined in equation 3.
Relation Extraction Re-ranking Model Optimization
While training D - REX we train
RR with labeled relations as supervision and use a cross-entropy loss, L RR RE , calculated in the same way as R in Equation 1.
Experimental Evaluation
In this section, we present an evaluation of DREX in comparison with baselines methods on the relation extraction and explanation extraction tasks.
5. Experimental settings
For our experiments, we re-implement the BERT S model from (Yu et al., 2020) as well as a new version which replaces BERT with RoBERTa.
In our paper, we refer to these models as R BERT and
R RoBERTa .
All models are implemented in PyTorchand Transformers(Wolf et al., 2020), trained using the AdamW optimizer (Loshchilov and Hutter, 2018).
All experiments were repeated five times and we report mean scores along with standard deviations.
D - REX models use a top-k of five and are initialized from the best performing models with the same backbone.
For example, D - REX BERT uses two copies of R BERT (Yu et al., 2020) to initialize the ranking and re-ranking models and EX BERT to initialize the explanation model.
When training
Joint , we do not calculate L EX for relational triples without a labeled trigger.
The full details of our training settings are provided in Appendix B.
DialogRE Dataset
We evaluate our models on the DialogRE English V2 dataset 4 which contains dialogues from the Friends TV show (Yu et al., 2020), details of which are in Table 1.
DREX models are trained with trigger supervision on less than 40% of the training data, and make no use of dev or test set triggers.
The learning signal for the remaining triples comes entirely from our rewards through a policy gradient.
[Table]
Triggers
[Table]
Evaluation Metrics
We adopt separate evaluations for relation and explanation extraction.
First, for relation extraction, we evaluate our models using F1 score, following Yu et al.
(2020), and additionally calculate the mean reciprocal rank (MRR), which provides further insight into a model’s performance.
For example, MRR is able to differentiate between a ground truth relation ranked 2nd or 10th, while the F1 score does not.
In the dialogRE dataset, multiple relations may hold between a single pair of entities, so we use a variation of MRR which considers all ground truth relations, rather than just the highest-ranked ground truth relation.
For explanation extraction, we focus mainly on manual evaluations, but also propose the LeaveOne-Out metric, introduced in section 5.4 for an ablation study.
5. Relation Extraction (RE) Evaluation
In Table 2, we compare the baseline RE model
R BERT with the methods presented in this paper.
We also compare with three other methods which use similarly sized language models, but additionally utilize graph neural networks (GNN): GDPNet(Xue et al., 2021), TUCORE-GCN BERT (Lee and Choi, 2021), and SocAoG(Qiu et al., 2021).
First, we see that even though D - REX is designed to introduce human-understandable explanations, it still has modest improvements over R BERT , which focuses on RE, while Joint has no significant improvement.
Next, we see a five point absolute improvement in F1 from the baseline model when using RoBERTa.
The trend from BERT to
RoBERTa is similar to results found by Lee and
Choi (2021), where changing from a BERT base model to RoBERTa Large (not shown here) improved their model performance significantly.
Additionally, we see a 3 point improvement from R to DREX when using RoBERTa (compared to 0.7 for
BERT), which we believe is due to the better per-
[Table]
forming ranking model, which allows for D - REX to rely more on the input explanations.
Finally, we see that by using GNNs, and task-specific dialogue representations, all three GNN-based methods can improve over the general BERT-based methods.
5. Explanation Extraction (EE) Evaluation Automatic Evaluation
Although the aim of this paper is not trigger prediction, for completeness and reproducibility, we include results on the test set of triggers in Appendix A.
Human Evaluation
To better understand how our model performs in extracting explanations and what challenges still exist, we perform two analyses; a comparative and an absolute analysis.
We consider two sets of data for evaluation: samples for the DialogRE test set where N o L abeled trigger exists ( NL ) and samples where the predicted explanation D iffers from the L abeled trigger ( DL ).
5.3. Comparative Analysis
In Table 3, we show the results for pairwise comparisons of explanations predicted by DREX RoBERTa against 3 baselines: random strings of 1-4 words, predictions from Joint RoBERTa , and labeled triggers.
For each comparison, we employcrowd-workers 5 , who were given the full dialogue, a natural language statement corresponding to a relational triple, and the two proposed explanations highlighted in the dialogue 6 .
The crowd-workers were asked to specify which of the highlighted explanations was most indicative of the relation, or
[Table] [Table]
where N o L abeled trigger exists ( NL ) and 100 where the predicted explanation D iffers from the L abel ( DL ).
they could be equal. For each comparison we use a majority vote, and if there was a three-way tie we consider the explanations to be equal.
We compare
D - REX with random strings and the joint model on 174 samples from NL , as well as 174 samples from
DL .
In Table 3 we see that for NL , D - REX produces explanations which were 4.2 times more likely to be outright preferred by crowd-workers than the joint model, suggesting that our reward functions properly guided the explanation policy to learn meaningful explanations on unlabeled data.
Surprisingly, we found that on over 12% of samples with labeled triggers, evaluators outright preferred
D - REX explanations over the ground truth trigger, suggesting that D - REX indeed finds some explanations which are better than the ground truth trigger.
In Appendix 5.5, we include 2 examples comparing explanations from D - REX and Joint .
5.3. Absolute Analysis
To better understand the quality of D - REX ’s explanations, we randomly sample 100 from both NL and DL for a fine-grained analysis.
We classify the explanations into 4 categories: not indicative, incorrect entity-pair, incorrect relation, and indicative.
"Indicative" and "Not indicative" have the obvious meanings, "Incorrect entity-pair" means that an explanation actually explains the correct relation, but between the incorrect entity-pair, and "Incorrect relation" means that the explanation indicates a relation different from the desired relation.
Table 4 shows the results.
Interestingly, we see in the NL set, that errors were equally likely to come
[Table]
from either an explanation indicating the relation for an incorrect entity-pair as for the incorrect relation altogether.
This is in contrast to the DL set, where D - REX was nearly half as likely to predict an explanation for an incorrect relation as it was for an incorrect entity-pair.
Additionally, in our fine-grained analysis, we also considered whether a relational triple was identifiable from the context alone and found that nearly 20% of the 200 samples had ambiguities which could not be resolved without outside knowledge.
This suggests that there is likely a maximum achievable relation extraction score on the DialogRE dataset under the current setting.
5. Ablation Study
To assess the benefit of each proposed reward individually, we perform an ablation study on the reward functions.
In order to study explanation quality automatically, we introduce a new metric for explanation quality; the Leave-One-Out metric.
The Leave-One-Out (LOO) metric has a theoretical basis in the works of Li et al.
(2016) and Ribeiro et al.
(2016), where Li et al.
(2016) use word erasure to determine a "word importance score".
Here we define LOO formally.
For a relation extraction model R , an explanation extraction model EX , and a dataset D , LOO is calculated as
[Equation]
where F1 R ( D ) is the F1 score of R on D and
D MASK ( EX ) is the dataset where explanations predicted by EX are replaced by mask tokens.
The
LOO metric calculates how essential the predicted explanations are to the ability of the relation extraction model.
To show that LOO is an appropriate measure of explanation quality, we compute the Pearson correlation coefficient between token F1 score and
LOO scores for models on labeled triggers, found in Table 6.
With 6 models trained on 5 random seeds each, we have 30 data points and a correlation coefficient of − 87 .
4 with p = 2 .
4 ∗ 10 − 8 .
Because we calculate the coefficient with respect to humanannotated triggers, this suggests that a low LOO correlates with explanations that humans would determine as indicative of the given relation.
For our experiments, we always calculate LOO using the baseline model, R BERT .
From the results in Table 5, we see that both reward functions benefit the final results.
Compared with R RoBERTa ,
D - REX RoBERTa gains 3 F1 points, but without the reranking reward, the model only gains 1.8 F1 score or 60% of the total possible improvement.
This performance loss demonstrates that the reranking reward is critical to attaining the best score in relation extraction.
Similarly, without the leave-one-out reward, the model’s explanation quality, measured in
LOO, is 1.5 points, or nearly 10% worse, demonstrating that the leave-one-out reward is beneficial in guiding the model to salient explanations. 5. Explanation Samples
Figure 3 shows two samples comparing explanations from D - REX and Joint .
In both examples, even though there was no labelled trigger, each model was able to predict an explanation which correlates with the relation.
Specifically, "engagement ring" and "got married" are related to the girl/boyfriend relation, and "in" and "mean in" can be associated with the visited_by relation. However, the bottom example shows that Joint did not consider the context surrounding it’s explanation. The conversation is about food, and the visited_by relation is not relevant. On the other hand, D - REX finds the phrase "you’re mean in", where "you’re" refers to speaker3, and "in" refers to "England".
This is clearly an explanation which indicates the correct relation between the correct entities.
5. Reduced Labels
All previous results use 100% of labeled triggers in the DialogRE dataset, which covers 40% of all relational triples.
To test how few labeled triggers
EX requires in order to learn meaningful explanations we ran a small scale experiment (1 random seed) using labeled triggers from only 5, 10, and 20% of relational triples.
However, in the small tests we ran, we found that at 20% labeled triggers the EX model mostly predicts no explanations.
Furthermore, at 10% and fewer labeled triggers, the model converges to the trivial solution in the explanation space which is to never predict any tokens.
We believe that this issue is due, in part, to two challenges: the search space over all possible start/end tokens is too large, and the policy gradient has a high variance.
Although these results may seem discouraging, we believe this challenge can be overcome in the future by using algorithms which reduce variance in the policy gradient and by initializing EX with a model pre-trained in span extraction.
Limitations and Future Work
Firstly, this study focuses on learning explanations as well as relations in dialogue and DialogRE is the only currently available dataset with annotations for both tasks.
A limitation of this study is the small scale at which we were able to test the methods.
A future direction would be to learn explanations on a different RE dataset and use the pre-trained model in D - REX , however it would be non-trivial for a model to transfer explanations learned on a wildly different domain.
Additionally, it is theoretically possible to train D - REX with no labeled triggers at all, however, we were unsuccessful and in Section 5.6 we discuss these and additional negative results.
This study focuses on relations and entities found in multi-party conversations, and while there are similarities between the dialogue domain, medical literature, and wikipedia (e.g., multi-entity, multi-relation), it is not clear whether the methods from this paper can transfer to other such domains.
We plan to investigate how well the proposed methods transfer to relations and entities in other domains such as news and web text (Zhang et al., 2017) and for other types of semantic relations as in Hendrickx et al.
(2010) or Yao et al.
We acknowledge that this study is Englishfocused, and it is not clear that these methods can transfer to languages in other families such as afroasiatic or sino-tibetan.
Additionally, we think that it would be very interesting to see how these methods perform on languages with very different linguistic features; for example, languages with inflection such as Finnish.
We leave non-English and multilingual variations of these methods to future work.
In this work, we do not focus on improving stateof-the-art trigger prediction.
However, we recognize that trigger annotation is labor-intensive, and a possible use of D - REX would be to use predicted labels as a form of weak supervision for a system whose goal is to improve on trigger prediction.
Related Work
Recently, there have been numerous information extraction tasks proposed which involve dialogues, including character identification (Zhou and Choi, 2018), visual coreference resolution (Yu et al., 2019), emotion detection (Zahiri and Choi, 2018).
New settings for relation extraction have also been proposed, such as web text (Ormándi et al., 2021) and, in many ways similar to dialogue, document text (Yao et al., 2019).
There have also been methods developed to include explanations in similar natural language understanding tasks (Camburu et al., 2018; Kumar and Talukdar, 2020; Liu et al., 2019a;
Lei et al., 2016).
There have even been methods developed which, similarly to our reranking, make use of an explanation as additional information (Hancock et al., 2018).
The work by Shahbazi et al.
is aligned with our study.
They also focus on relation extraction with explanations; however, their method is based on distant supervision from bags of sentences containing an entity-pair.
Due to the cross-sentence nature of relations in dialogue, their method is not applicable here, although we draw inspiration from their work.
They explain their model by considering the salience of a sentence to their model’s prediction, similarly to our leave-one-out reward.
Also relevant to our work is that by Bronstein et al..
Their work focuses on the task of semisupervised event trigger labeling, which is very similar to our semi-supervised prediction of relation explanations.
In their work, they use only a small seed set of triggers and use a similarity-based classifier to label triggers for unseen event types.
Finally, there have been multiple recent works in dialogue RE which perform quite well by using graph neural networks (Xue et al., 2021; Qiu et al., 2021; Lee and Choi, 2021).
However, they focus only on RE and not on explaining the relations.
Conclusion
In this work, we demonstrated that not only is it possible to extract relation explanations from multiparty dialogues, but these explanations can in turn be used to improve a relation extraction model.
We formulated purpose-driven reward functions for training the explanation model and demonstrated their importance in learning high quality explanations.
Our proposed approach, D - REX , is powered by a very simple reformulation of the traditional relation extraction task into a re-ranking task.
Ethical and Social Considerations
The methods proposed in this work on their own are not nefarious, however, proposed explanations should not be blindly accepted as fact.
For the same reasons that language models may have ethical and social risks, so may our algorithm which is built on top of such language models.
While we test only on TV show dialogues, were this technology to be put to use in non-scripted, real life conversations, there would need to be very thorough analysis of any ethical risks that the proposed explanations may have.
A Trigger prediction
In Table [Table] we compare our methods for supervised explanation extraction with D - REX .
Interestingly, we find that the joint model achieves the lowest
F1 score for both the BERT and RoBERTa models.
Joint BERT scores nearly 20 points below its counterpart BERT model, while the Joint RoBERTa model cuts that difference to just over 15 points below its RoBERTa counterpart.
On the other hand,
D - REX maintains a token F1 score within 10 points of its counterpart even though it has been trained to generalize beyond the labeled triggers.
B Hyperparameters
All models are trained using the AdamW optimizer (Loshchilov and Hutter, 2017) with a learning rate of 3 e - 5 and batch sizes of 30 .
To determine the best learning rate, R and EX models were trained using learning rates in { 3 e - 6 , 1 e - 5 , 3 e - 5 , 1 e - 4 }.
The best learning rate, 3 e - 5 , was determined by performance on a held out validation dataset.
Baseline models ( R , EX , and Joint ) are trained for at most 30 epochs and we use validation-based early stopping to determine which model to test.
D - REX models are trained for at most 30 additional epochs with the best model determined based on relation extraction
F1 scores computed on validation data.
We found the best validation result to always occur within the first 30 epochs.
All experiments were repeated five times and we report the mean score along with standard deviation.
To train the joint model, we do not calculate L EX for relational triples which do not have a labeled trigger and we select α from {0.25,0.5,0.75} and set α to 0.5 based on validation performance.
C Crowd-Worker Sample
In Figure 4, we show a sample HIT that was provided to crowd-workers.
Each crowd-worker was shown three examples.
The layour is as follows: the top always asks the worker to decide which of the highlighted texts is a better indication of the relation.
Next, a natural language interpretation of the relational triple is given, in this case, "Speakerand Speaker 1 are (or were) lovers".
Then, we show the entire dialogue along with highlighted spans of text for each explanation.
Finally, at the bottom, we always provide the user with three choices: yellow is better, equal, or orange is better, where the user is only allowed to select one option.