Das Erlernen von Charakterdarstellungen verbessert das Dialogverständnis

In dieser Studie stellen wir einen neuartigen Ansatz zur Verbesserung der Dialoggenerierung durch die Nutzung des Lernens der Charakterdarstellung vor. Unser Modell basiert auf einem umfangreichen Datensatz chinesischer Romane und modelliert explizit die Beziehung zwischen Charakteren innerhalb der Geschichte. Dadurch kann unser Modell einen kohärenteren und vernünftigeren Dialog generieren, indem es die Motivationen und Interaktionen verschiedener Charaktere berücksichtigt. Wir evaluieren unser Modell anhand von zwei Dialoggenerierungsaufgaben – maskierter Dialoggenerierung und Dialogsprechererkennung – und demonstrieren seine Überlegenheit gegenüber starken Basislinien. Unsere Arbeit unterstreicht die Bedeutung des Lernens der Charakterdarstellung für die Dialoggenerierung und eröffnet neue Wege für zukünftige Forschung.

Authors:

Autoren:

(1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(1) Jianzhu Yao, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Table of Links Abstract and Intro Related Works DIALSTORY Dataset Proposed Tasks Methodology Experiments Discussion Future Work Conclusion Limitations and References 6 Experiments 6.1 Masked Dialogue Generation Implementation Details To conduct experiments on the masked dialogue generation task, we decide the hyper-parameters based on the performance of the validation set. We train Shao et al. (2021)’s BART model for 4.6 epochs with a 1e-4 learning rate for 1 day, and for our model, we train it for 5.6 epochs with a 1e-4 learning rate for 1 day. All baselines and our model are trained using the Adam optimizer. During the training process for our method, we computed the selection coverage of characters within a single story. And it showed that in every 1000 training steps, the coverage of different characters ranged from 98.64% to 99.00%, which meant nearly all the characters are selected during training, and all the characters contributed to the generated dialogue. It further proved that the argmax in Eq. 3 operation doesn’t break the gradient progress when training for this task. Automatic Evaluation Following previous works, we use several standard, widely used automatic evaluation metrics. We use BLEUn (Papineni et al., 2002) to measure the average word overlap between each generated and groundtruth dialogue turn (n=1,2), and Distinct-n (Li et al., 2015) to evaluate n-gram diversity of generated dialogue turns (n=2,3,4). To be more specific, for the coherence classifier, we construct the training and validation sets by randomly shuffling the order of dialogue turns and keeping other content in the correct order. We regard the perturbed story as a negative example and the original story as a positive example. We sample another 195k stories (except those in DIALSTORY) from the novels of Guan et al. (2022) to construct the training set (190k examples) and the validation set (5k examples). We train the model for 4 epochs with a 2e-5 learning rate and a 16- batch size, using the Adam optimizer. During the evaluation, we consider an example coherent when the probability of being coherent predicted by the classifier is greater than 0.5. We use the ratio of outputs (along with the input) that are classified as coherent by the classifier to all generated outputs as the coherence score. The result of the automatic evaluation is presented in Table 3. According to the table, compared to the BART baseline, our model consistently generates more word overlaps with ground truth and achieves better diversity under the guidance of character representations, which means our model can generate more diverse but not commonplace responses. Figure 3 plots the coherence score varying with the number of masked dialogue turns. The result shows that our model gets a higher coherence score than BART when required to generate more than seven turns of dialogue in one story. Manual Evaluation We conduct a pairwise comparison between our model and the BART baseline. We randomly select 100 examples from the test set. For each pair of outputs along with the input, we ask three annotators to give a preference (win, lose and tie) in terms of fluency, coherence, and informativeness. All the annotations are native Chinese speakers. We adopt majority voting to make final decisions among the annotators. The three aspects of manual evaluation are as follows: Fluency: Grammatical correctness and intra-sentence linguistic quality.

(4) Minlie Huang, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Linkverzeichnis Zusammenfassung und Einführung Verwandte Arbeiten DIALSTORY-Datensatz Vorgeschlagene Aufgaben Methodik Experimente Diskussion Zukünftige Arbeit Schlussfolgerung Einschränkungen und Referenzen 6 Experimente 6.1 Details zur Implementierung der maskierten Dialoggenerierung Um Experimente zur Aufgabe der maskierten Dialoggenerierung durchzuführen, legen wir die Hyperparameter basierend auf der Leistung von fest der Validierungssatz. Wir schulen Shao et al. (2021)‘s BART-Modell für 4,6 Epochen mit einer 1e-4-Lernrate für 1 Tag, und für unser Modell trainieren wir es für 5,6 Epochen mit einer 1e-4-Lernrate für 1 Tag. Alle Basislinien und unser Modell werden mit dem Adam-Optimierer trainiert. Während des Trainingsprozesses für unsere Methode haben wir die Auswahlabdeckung von Charakteren innerhalb einer einzelnen Geschichte berechnet. Und es zeigte sich, dass in allen 1000 Trainingsschritten die Abdeckung verschiedener Charaktere zwischen 98,64 % und 99,00 % lag, was bedeutete, dass fast alle Charaktere während des Trainings ausgewählt wurden und alle Charaktere zum generierten Dialog beitrugen. Es wurde weiterhin bewiesen, dass der argmax in Gl. 3-Vorgang unterbricht den Gradientenfortschritt beim Training für diese Aufgabe nicht. Automatische Bewertung Im Anschluss an frühere Arbeiten verwenden wir mehrere standardmäßige, weit verbreitete automatische Bewertungsmetriken. Wir verwenden BLEUn (Papineni et al., 2002), um die durchschnittliche Wortüberlappung zwischen jeder generierten und jeder Groundtruth-Dialogrunde (n=1,2) zu messen, und Distinct-n (Li et al., 2015), um die N-Gramm-Diversität zu bewerten der generierten Dialogrunden (n=2,3,4). Genauer gesagt erstellen wir für den Kohärenzklassifikator die Trainings- und Validierungssätze, indem wir die Reihenfolge der Dialogrunden zufällig mischen und andere Inhalte in der richtigen Reihenfolge halten. Wir betrachten die verstörte Geschichte als negatives Beispiel und die ursprüngliche Geschichte als positives Beispiel. Wir probieren weitere 195.000 Geschichten (außer denen in DIALSTORY) aus den Romanen von Guan et al. (2022), um den Trainingssatz (190.000 Beispiele) und den Validierungssatz (5.000 Beispiele) zu erstellen. Wir trainieren das Modell für 4 Epochen mit einer Lernrate von 2e-5 und einer Batchgröße von 16 unter Verwendung des Adam-Optimierers. Bei der Auswertung betrachten wir ein Beispiel als kohärent, wenn die vom Klassifikator vorhergesagte Wahrscheinlichkeit der Kohärenz größer als 0,5 ist. Wir verwenden das Verhältnis der Ausgaben (zusammen mit der Eingabe), die vom Klassifikator als kohärent eingestuft werden, zu allen generierten Ausgaben als Kohärenzbewertung. Das Ergebnis der automatischen Auswertung ist in Tabelle 3 dargestellt. Laut Tabelle generiert unser Modell im Vergleich zur BART-Basislinie durchgängig mehr Wortüberschneidungen mit der Grundwahrheit und erreicht unter der Anleitung von Zeichendarstellungen eine bessere Diversität, was bedeutet, dass unser Modell generieren kann vielfältigere, aber nicht alltägliche Antworten. In Abbildung 3 ist der Kohärenzwert dargestellt, der mit der Anzahl der maskierten Dialogrunden variiert. Das Ergebnis zeigt, dass unser Modell einen höheren Kohärenzwert als BART erhält, wenn mehr als sieben Dialogrunden in einer Geschichte generiert werden müssen. Manuelle Bewertung Wir führen einen paarweisen Vergleich zwischen unserem Modell und der BART-Basislinie durch. Wir wählen zufällig 100 Beispiele aus dem Testset aus. Für jedes Ausgabepaar zusammen mit der Eingabe bitten wir drei Annotatoren, eine Präferenz (Sieg, Niederlage und Unentschieden) in Bezug auf Sprachkompetenz, Kohärenz und Informationsgehalt anzugeben. Alle Anmerkungen stammen von chinesischen Muttersprachlern. Wir wenden Mehrheitsentscheidungen an, um endgültige Entscheidungen unter den Kommentatoren zu treffen. Die drei Aspekte der manuellen Bewertung sind wie folgt: Sprachkompetenz: Grammatische Korrektheit und sprachliche Qualität innerhalb des Satzes.

Coherence: Inter-sentence relatedness, causal and temporal dependencies. We judge the coherence between the story and a dialogue turn by following the criterion in Table 5. We add the scores of all the generated dialogue turns in a story to get the overall coherence score of the story, which is then used to compare with each other.

Kohärenz: Bezogenheit zwischen Sätzen, kausale und zeitliche Abhängigkeiten. Wir beurteilen die Kohärenz zwischen der Geschichte und einer Dialogrunde, indem wir das Kriterium in Tabelle 5 befolgen. Wir addieren die Bewertungen aller generierten Dialogrunden in einer Geschichte, um die Gesamtkohärenzbewertung der Geschichte zu erhalten, die dann zum Vergleich mit jeder einzelnen verwendet wird andere.

Informativeness: Interesting, diverse and rich details. As shown in Table 4, all the results show moderate (κ > 0.4) agreement, which shows our model outperforms the BART baseline significantly in dialogue informativeness and coherence. Case Study Figure 5 showed two examples to investigate how learning character representations can help our model generate more coherent dialogue. We found that our model can better model the relationship between different characters and the direction of the storyline. For example, in the first case, we can see that the BART’s generation confuses different characters’ fathers, while our model captures the relationship between different characters, and generates proper responses for the corresponding characters, which also moves the plot forward. And in the second case, we can see that BART’s generation is commonplace and contradicts the plot development. In contrast, our model captures the intentions of the speaker and the development trend of the plot, generating an appropriate and coherent response. Since these two models use the same pretrained weight, we can infer that the character modeling module leverages the coherent and reasonable generation. We also summarize four error types of the generated dialogue turn for the DialGen task: (1) Intersentence Contradiction; (2) Inter-sentence Repetition; (3) Intra-sentence Contradiction; (4) Intrasentence Repetition. We show the typical corresponding cases in Figure 4. We conducted a quantitative analysis of those 4 error types on our model’s generation. We analyzed 20 stories with 103 dialog turns and the results are shown in Figure 6. We found that both our model and BART suffer from these errors, suggesting that there is still space for model improvement, especially in the inter-sentence repetition. 6.2 Dialogue Speaker Recognition Implementation Details To conduct experiments on the speaker recognition task, we decide the hyper-parameters based on the performance of the validation set. For the BART baseline and our approach, we insert a mask token before each dialogue needed to be predicted, and a person id token before and after each character name span. Then, we insert all the unique person id tokens before the input stories as different options, and make predictions based on the cos similarity of option tokens and mask tokens. We train Shao et al. (2021)’s BART model for 30 epochs with a 5e-5 learning rate for 3 days, For encoder-only baselines, we implemented BERT, RoBERTa, and MacBERT and trained them for 15 epochs wity a le-5 learning rate for 2 days. For our model, we train it for 22 epochs with a le-6 learning rate for 2 days. All baselines and our model are trained using the Adam optimizer. Metrics We evaluate the DialSpk task using two automatic metrics including dialogue-level accuracy (DAC) and story-level accuracy (SAC). DAC is calculated as the ratio of the correct predictions to the total number of specifies dialogue turns, while SAC is the ratio of the number of stories where all dialogue turns are correctly predicted to the number of all test examples. These two metrics provide the evaluation for dialogue understanding with different granularities. Results As shown in Table 7, our model outperforms all the baselines significantly (p< 0.01, Wilcoxon signed-rank test) on both DAS and SAC scores, suggesting the benefit of learning character representations. We tested the accuracy of automatic training set annotations, and the DAC/SAC scores are 86.78%/67.80%. Together with the model’s performance on the test set, we can see the automatic annotation for the training set is of good quality. We also conducted the human prediction experiment, and the DAC/SAC scores are 97.90%/90.70%, which are much higher than the best model. So there is much room for further improvement for machine-based approaches. This paper is available on arxiv under CC 4.0 DEED license. [2] https://huggingface.co/fnlp/ bart-base-chinese [3] https://huggingface.co/bert-base-chinese [4] https://huggingface.co/hfl/ chinese-roberta-wwm-ext [5] https://huggingface.co/hfl/ chinese-macbert-base Authors:

Autoren:

(1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(1) Jianzhu Yao, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Authors:

Autoren:

Authors:

Autoren:

(1) Jianzhu Yao, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(1) Jianzhu Yao, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(2) Ziqi Liu, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(3) Jian Guan, The CoAI Group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology;

(4) Minlie Huang, The CoAI group, Tsinghua University, Beijing, China Department of Computer Science and Technology, Tsinghua University, Beijing, China Beijing National Research Center for Information Science and Technology. Table of Links Abstract and Intro Abstract and Intro Related Works Related Works DIALSTORY Dataset DIALSTORY Dataset Proposed Tasks Proposed Tasks Methodology Methodology Experiments Experiments Discussion Discussion Future Work Future Work Conclusion Conclusion Limitations and References Limitations and References 6 Experiments 6.1 Masked Dialogue Generation Implementation Details To conduct experiments on the masked dialogue generation task, we decide the hyper-parameters based on the performance of the validation set. We train Shao et al. (2021)’s BART model for 4.6 epochs with a 1e-4 learning rate for 1 day, and for our model, we train it for 5.6 epochs with a 1e-4 learning rate for 1 day. All baselines and our model are trained using the Adam optimizer. Implementation Details During the training process for our method, we computed the selection coverage of characters within a single story. And it showed that in every 1000 training steps, the coverage of different characters ranged from 98.64% to 99.00%, which meant nearly all the characters are selected during training, and all the characters contributed to the generated dialogue. It further proved that the argmax in Eq. 3 operation doesn’t break the gradient progress when training for this task. Automatic Evaluation Following previous works, we use several standard, widely used automatic evaluation metrics. We use BLEUn (Papineni et al., 2002) to measure the average word overlap between each generated and groundtruth dialogue turn (n=1,2), and Distinct-n (Li et al., 2015) to evaluate n-gram diversity of generated dialogue turns (n=2,3,4). Automatic Evaluation To be more specific, for the coherence classifier, we construct the training and validation sets by randomly shuffling the order of dialogue turns and keeping other content in the correct order. We regard the perturbed story as a negative example and the original story as a positive example. We sample another 195k stories (except those in DIALSTORY) from the novels of Guan et al. (2022) to construct the training set (190k examples) and the validation set (5k examples). We train the model for 4 epochs with a 2e-5 learning rate and a 16- batch size, using the Adam optimizer. During the evaluation, we consider an example coherent when the probability of being coherent predicted by the classifier is greater than 0.5. We use the ratio of outputs (along with the input) that are classified as coherent by the classifier to all generated outputs as the coherence score. The result of the automatic evaluation is presented in Table 3. According to the table, compared to the BART baseline, our model consistently generates more word overlaps with ground truth and achieves better diversity under the guidance of character representations, which means our model can generate more diverse but not commonplace responses. Figure 3 plots the coherence score varying with the number of masked dialogue turns. The result shows that our model gets a higher coherence score than BART when required to generate more than seven turns of dialogue in one story. Manual Evaluation We conduct a pairwise comparison between our model and the BART baseline. We randomly select 100 examples from the test set. For each pair of outputs along with the input, we ask three annotators to give a preference (win, lose and tie) in terms of fluency, coherence, and informativeness. All the annotations are native Chinese speakers. We adopt majority voting to make final decisions among the annotators. The three aspects of manual evaluation are as follows: Manual Evaluation Fluency: Grammatical correctness and intra-sentence linguistic quality. Coherence: Inter-sentence relatedness, causal and temporal dependencies. We judge the coherence between the story and a dialogue turn by following the criterion in Table 5. We add the scores of all the generated dialogue turns in a story to get the overall coherence score of the story, which is then used to compare with each other.

Kohärenz: Bezogenheit zwischen Sätzen, kausale und zeitliche Abhängigkeiten. Wir beurteilen die Kohärenz zwischen der Geschichte und einer Dialogrunde, indem wir das Kriterium in Tabelle 5 befolgen. Wir addieren die Bewertungen aller generierten Dialogrunden in einer Geschichte, um die Gesamtkohärenzbewertung der Geschichte zu erhalten, die dann zum Vergleich mit jeder einzelnen verwendet wird andere.

Informativeness: Interesting, diverse and rich details. Fluency: Grammatical correctness and intra-sentence linguistic quality. Fluency : Grammatical correctness and intra-sentence linguistic quality. Fluency Coherence: Inter-sentence relatedness, causal and temporal dependencies. We judge the coherence between the story and a dialogue turn by following the criterion in Table 5. We add the scores of all the generated dialogue turns in a story to get the overall coherence score of the story, which is then used to compare with each other.

Kohärenz: Bezogenheit zwischen Sätzen, kausale und zeitliche Abhängigkeiten. Wir beurteilen die Kohärenz zwischen der Geschichte und einer Dialogrunde, indem wir das Kriterium in Tabelle 5 befolgen. Wir addieren die Bewertungen aller generierten Dialogrunden in einer Geschichte, um die Gesamtkohärenzbewertung der Geschichte zu erhalten, die dann zum Vergleich mit jeder einzelnen verwendet wird andere.

Coherence : Inter-sentence relatedness, causal and temporal dependencies. We judge the coherence between the story and a dialogue turn by following the criterion in Table 5. We add the scores of all the generated dialogue turns in a story to get the overall coherence score of the story, which is then used to compare with each other. Coherence Informativeness: Interesting, diverse and rich details. Informativeness : Interesting, diverse and rich details. Informativeness As shown in Table 4, all the results show moderate (κ > 0.4) agreement, which shows our model outperforms the BART baseline significantly in dialogue informativeness and coherence. Case Study Figure 5 showed two examples to investigate how learning character representations can help our model generate more coherent dialogue. We found that our model can better model the relationship between different characters and the direction of the storyline. For example, in the first case, we can see that the BART’s generation confuses different characters’ fathers, while our model captures the relationship between different characters, and generates proper responses for the corresponding characters, which also moves the plot forward. And in the second case, we can see that BART’s generation is commonplace and contradicts the plot development. In contrast, our model captures the intentions of the speaker and the development trend of the plot, generating an appropriate and coherent response. Since these two models use the same pretrained weight, we can infer that the character modeling module leverages the coherent and reasonable generation. Case Study We also summarize four error types of the generated dialogue turn for the DialGen task: (1) Intersentence Contradiction; (2) Inter-sentence Repetition; (3) Intra-sentence Contradiction; (4) Intrasentence Repetition. We show the typical corresponding cases in Figure 4. We conducted a quantitative analysis of those 4 error types on our model’s generation. We analyzed 20 stories with 103 dialog turns and the results are shown in Figure 6. We found that both our model and BART suffer from these errors, suggesting that there is still space for model improvement, especially in the inter-sentence repetition. 6.2 Dialogue Speaker Recognition Implementation Details To conduct experiments on the speaker recognition task, we decide the hyper-parameters based on the performance of the validation set. For the BART baseline and our approach, we insert a mask token before each dialogue needed to be predicted, and a person id token before and after each character name span. Then, we insert all the unique person id tokens before the input stories as different options, and make predictions based on the cos similarity of option tokens and mask tokens. We train Shao et al. (2021)’s BART model for 30 epochs with a 5e-5 learning rate for 3 days, For encoder-only baselines, we implemented BERT, RoBERTa, and MacBERT and trained them for 15 epochs wity a le-5 learning rate for 2 days. For our model, we train it for 22 epochs with a le-6 learning rate for 2 days. All baselines and our model are trained using the Adam optimizer. Metrics We evaluate the DialSpk task using two automatic metrics including dialogue-level accuracy (DAC) and story-level accuracy (SAC). DAC is calculated as the ratio of the correct predictions to the total number of specifies dialogue turns, while SAC is the ratio of the number of stories where all dialogue turns are correctly predicted to the number of all test examples. These two metrics provide the evaluation for dialogue understanding with different granularities. Metrics Results As shown in Table 7, our model outperforms all the baselines significantly (p< 0.01, Wilcoxon signed-rank test) on both DAS and SAC scores, suggesting the benefit of learning character representations. We tested the accuracy of automatic training set annotations, and the DAC/SAC scores are 86.78%/67.80%. Together with the model’s performance on the test set, we can see the automatic annotation for the training set is of good quality. We also conducted the human prediction experiment, and the DAC/SAC scores are 97.90%/90.70%, which are much higher than the best model. So there is much room for further improvement for machine-based approaches. Results This paper is available on arxiv under CC 4.0 DEED license. This paper is available on arxiv under CC 4.0 DEED license. available on arxiv [2] https://huggingface.co/fnlp/ bart-base-chinese [3] https://huggingface.co/bert-base-chinese [4] https://huggingface.co/hfl/ chinese-roberta-wwm-ext [5] https://huggingface.co/hfl/ chinese-macbert-base