Reducing Sensitivity on Speaker Names for Text Generation from Dialogues
Year: 2023

Abstract:
Changing speaker names consistently throughout a dialogue should not affect its meaning and corresponding outputs for text generation from dialogues. However, pre-trained language models, serving as the backbone for dialogue-processing tasks, have shown to be sensitive to nuances. This may result in unfairness in real-world applications. No comprehensive analysis of this problem has been done in the past. In this work, we propose to quantitatively measure a model’s sensitivity on speaker names, and comprehensively evaluate a number of known methods for reducing speaker name sensitivity, including a novel approach of our own. Extensive experiments on multiple datasets provide a benchmark for this problem and show the favorable performance of our approach in sensitivity reduction and quality of generation.

heading: 1 Introduction
text: The safety and fairness issue of generations from dialogue models is a crucial concern in real applications. Previous work focuses on response generation from open-ended dialogue systems (Xu et al., 2020; Henderson et al., 2018), such as offensive contents (Baheti et al., 2021), gender bias (Liu et al., 2020; Dinan et al., 2020) and other discriminated behavior (Sheng et al., 2021; Smith and Williams, 2021). For other text generation tasks where the whole dialogue is provided and the output shouldn’t go beyond the dialogue, such as dialogue summarization (Gliwa et al., 2019) and dialogue reading comprehension (Li et al., 2020), the fairness issue is still unexplored.
In these tasks, the input dialogues are selfcontained, and the names of the speakers do not carry any connotation from outside of the dialogue. Therefore, changing the speaker names consistently in a dialogue should not affect the meanings of the
∗ The corresponding author.
are underlined and incorrect contents are italicized.
dialogue and the desired outputs. This contrasts with response generation, where the dialogue is in progress and the output is expected to be different in styles or contents for various speakers. Taking dialogue summarization (Gliwa et al., 2019; Chen et al., 2021) as an example for text generation from dialogues, it focuses on generating concise “who-did-what” summaries in the third person. In Fig. 1, the two dialogues are identical except for the speaker names. The two summaries are expected to be the same modulo the speaker names.
Unfortunately, models nowadays, following the pretrain-finetune paradigm, are sensitive to trivial changes, which has been verified in other tasks. In relation extraction, spurious correlations between entity mentions and relations lead to entity bias (Zhang et al., 2018, 2017; Wang et al., 2022b). Other similar work includes the analysis of robustness by entity renaming for machine reading comprehension models on narrative texts (Yan et al., 2022) and name biases in machine transla-
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tion with inflected languages (Wang et al., 2022a), like German. Besides, Shwartz et al. (2020) claims that pre-trained language models do not treat given names as interchangeable or anonymous, showing unfairness in reading comprehension.
Obviously, dialogue understanding models are sensitive to speaker names according to Fig. 1 as well. The model tends to generate different information given different speaker names, such as “don’t want to go” and “doesn’t like them”. Incorrect content, “... Betsy don’t want to go”, is generated with the first group of speakers, while not with the other group. According to our pilot experiment with the vanilla BART fine-tuned on SAMSum, around 74.00% of generations are changed by switching speaker names and 69.82% among them are due to distinct contents. Such uneven performances create unfairness among different speakers, especially in the aspect of information allocation. The model may also catch latent properties in names (Romanov et al., 2019) and lead to discrimination, raising the importance of research on the sensitivity on speaker names.
Previous work has also mentioned this problem. Different data pre-processing approaches are adopted during the construction of datasets to avoid using speaker names, such as “A” or “B” in Li et al. (2017). Khalifa et al. (2021) replace speaker names with more common and frequent names that the model may have seen during pre-training. Data augmentation by changing speaker names is adopted by Liu and Chen (2021). However, all of them only attempted to attack this problem subjectively, without quantitive analysis and fair comparisons.
In this work, we systematically analyze speaker name sensitivity in text generation from dialogues. We define the speaker name sensitivity and divide the approaches into offline and online ones. Then, we propose two novel insensitivity losses, helping to reduce attention and hidden state distances of the same dialogue with different speaker names for transformer-based models during finetuning. These losses can be used in both kinds of approaches. Results on several tasks show that our losses reduce the sensitivity and get better generations. In summary, our contributions are:
• We are the first to investigate the speaker name sensitivity in text generation from dialogues (Sec. 2.1) with all of the codes and results open-sourced at https://github.com/ JiaQiSJTU/SpeakerNameSensitivity.
• We introduce two novel insensitivity losses as auxiliary training objectives for reducing sensitivity during fine-tuning (Sec. 3).
• Experiments on different tasks provide a benchmark with comprehensive analysis on speaker name sensitivity, and show state-ofthe-art performances of our approach (Sec. 5).

heading: 2 Background

heading: 2.1 Speaker Name Sensitivity
text: Speaker name sensitivity is the differences in the generations by a model, given the identical dialogues except for different speaker names. We define it as follows.
Let d denote the input dialogue. c denotes other input content, which can be empty for tasks like dialogue summarization, or a piece of text such as a question for reading comprehension. p refers to the set of speakers names in d. f is a one-to-one mapping which maps p into a set of names p′ from a name pool P consisting of a set of candidate names to be substituted into the samples. The names p′ are sampled under the uniform distribution without the loss of generality. The speaker name sensitivity SS of a generation modelM(·) on this sample is:
SS(M|d, c) = δ({M(Rep(d, c|f)) |∀f : p→ p′, p′ ⊆ P}) (1)
where Rep(·) replaces names in the sample given f , i.e., from p to p′. δ(·) quantifies the differences among generations.
Then, the sensitivity SS of a modelM(·) is the expectation E of over all samples from the realworld distribution D:
SS(M) = E(d,c)∼D[SS(M|d, c)] (2)
In practice, a dialogue dataset is regarded as a sampling from D for evaluations. Each sample in the dataset is provided with a reference output o for supervised training. We use Dtr, Dva and Dte to refer to training, validation and test sets. See detailed implementations and metrics in Sec. 4.1.

heading: 2.2 Existing Approaches
text: We investigate existing approaches that target on reducing the sensitivity and classify them into offline ones and online ones, where the former chases to reduce the sensitivity by exploring better model parameters and the latter pursues insensitivity by
unification or simplification of input data. Thus, data processing steps are required before inputting into the model and after the inference during the test time and speaker names in Dtr, Dva and Dte are all changed for online approaches. The model needs fine-tuning for both approaches.
Offline approaches include: Embedding Layer(Emb): Similar to (Gu et al., 2020) and (He et al., 2021), an additional embedding layer can be adopted for representing whether the model should be sensitive to corresponding tokens. 2 embeddings are learned during fine-tuning.
Augmentation (Aug): Liu and Chen (2021) proposed to do data augmentation by exchanging speaker names in training samples with names from Dtr. They aim to reduce unexpected inductive bias caused by speaker names, which is similar to our goal. The model is fine-tuned with augmented training data while Dva and Dte remain unchanged.
Online approaches are: ID: Some works (Cui et al., 2020; Li et al., 2017) replace speaker names with predefined IDs to avoid name bias. We use “Speaker[NUM]” similarly to Kim et al. (2019) and Chen et al. (2021), which is close to words seen during pre-training and fits different numbers of speakers. “[NUM]” is the index of a speaker’s first occurrence.
Frequent (Fre): This refers to the approach proposed in Khalifa et al. (2021). They use 100 frequent male and 100 frequent female names online1 as the pool P for sampling replacements. This approach can be combined with Aug into FreAug.

heading: 3 Proposed Approach
text: We focus on the widely-accepted encoder-decoder architecture for pre-trained generation models and design two auxiliary insensitivity losses to take full advantage of augmented data on top of Aug. Given the dialogue sample with different speaker names, a model outputs distinct generations due to its different internal behaviors. Therefore, penalizing unexpected internal differences should help the model behave consistently and reduce the sensitivity.
With this intuition, we propose the crossattention loss and the decoder-hidden-state loss. An illustration for them is in Appendix A. The former corresponds to cross-attention distributions that help the decoder make a soft information selection among encoder hidden states at each step and should be similar with different speaker names.
1https://www.ssa.gov/oact/babynames/decades/century.html
The latter is based on the final decoder hidden states which are expected to be the same under the default teacher-forcing training strategy except for the speaker name tokens. We didn’t consider the encoder attentions since according to our pilot analysis of the vanilla BART, the cross attentions distance of the different predictions is around 1.5 times of the same ones. However, there are no differences in the encoder attentions. Other intermediate hidden states are excluded since they are all affected by different input embeddings of speaker names, except that the final decoder hidden states are sure to be the same.

heading: 3.1 Cross-attention Insensitivity Loss
text: We denote a model’s input and output length, i.e., the number of tokens, as din and dout. During training, the cross attentions calculated for each output token are collected as CA ∈ RN×dout×din. N is the number of heads for the multi-head attention mechanism, determined by the configuration of pre-trained models. We apply average pooling over the dimension of dout, to get the overall attention over the input tokens CA ∈ RN×din.
Given an original sample {di, ci, oi}, we construct K − 1 augmented samples by replacing speaker names. The averaged attentions for all samples are {CAk}Kk=1. Since it is a default that each sample should go through the tokenizer before inputting to the model, {dink}Kk=1 are not guaranteed to be identical in two cases. First, names may be tokenized into different token counts. For example, “John” and “Robinson” are tokenized into {“John”} and {“Rob”, “inson”} by BART tokenizer. Replacing “John” with “Robinson” in di will increase the sequence length. Second, long inputs may be truncated at different tokens. So, we consider two corresponding functions for unification:
• Sum(·) sums up the attention values of tokens belonging to an occurrence of a speaker name.
• Pad(·) pads attentions into the same length dinu by concatenating zeros, which means that this part of contents is missing.
The unified {CAk}Kk=1 is represented as {C̃Ak}Kk=1, where C̃Ak ∈ RN×dinu .
Finally, the loss is calculated as:
Lca = 1 K(K − 1) K∑
k=1
K∑
l=1,l 6=k loss(C̃Ak, C̃Al)
(3)
where loss(·) measures the distances between a pair of attentions.

heading: 3.2 Decoder-hidden-state Insensitivity Loss
text: Similarly, hidden states of the decoder’s final output for all samples can be denoted as {DHk}Kk=1, whereDHk ∈ RH×doutk andH represents the hidden size. The lengths of them also vary due to the above two cases. We adopt two different functions:
• Del(·) ignores the hidden states whose predicted tokens belong to a speaker name.
• Trunc(·) truncates the redundant hidden states at the end without the paired ones.
Thus, the unified {DHk}Kk=1 is represented as {D̃Hk}Kk=1, where D̃Hk ∈ RH×doutu .
The loss is defined as:
Ldh = 1 K(K − 1) K∑
k=1
K∑
l=1,l 6=k loss(D̃Hk, D̃H l)
(4) We adopted the mean square error for both losses.

heading: 3.3 Learning Objective
text: Lca and Ldh are added to the vanilla generation loss Lgen with hyper-parameters α and β:
Ltotal = Lgen + αLca + βLdh (5)
The insensitivity losses are only auxiliary finetuning objectives, leaving the inference time unchanged. They can be added on top of both Aug and FreAug, denoted as Ins and FreIns.

heading: 4 Experimental Setup
text: We define the evaluation metrics for sensitivity, introduce multiple text generation tasks with dialogue data and present implementation details.

heading: 4.1 Evaluation Metrics for Sensitivity
text: We uniformly sample names from P , which is specified later, to realize f without the loss of generality and re-sample the name if it is not in p but in the conversation. We avoid changing names mentioned during the conversation in case they are grounded entities. Since it’s impossible to enumerate all possible f , we choose to substitute names of samples in Dte for T = 5 times. It should be noted that varying names in test data is different from the augmentation approach. The additional test data is
fixed once constructed for comparing approaches by quantitatively measuring the sensitivity.
We introduce three kinds of δ(·) with taskspecific evaluation metric Score(·), such as Rouge and BertScore for dialogue summarization, and measure the speaker name sensitivity of a model similar to Prabhakaran et al. (2019)’ work. Pairwise Sensitivity(S-*) is defined as:
EN te i=1E T t1=1E T t2=1,t1 6=t2 [1− Score(ô t1 i , ô t2 i )] (6)
ôti is the generation where replaced names are changed back for evaluation. N te is the number of samples in Dte. E(·) is the mean operator.
Dialogue models are also expected to get the same scores with task-specific evaluation metrics compared with the reference o. So, we can also add o as the input of δ(·) in Eq. 1 and define the following two metrics: Score Range (R-*) as
EN te i=1 [ max({Score(oi, ôti)|Tt=1}) −min({Score(oi, ôti)|Tt=1})]
(7)
and Score Deviation (D-*) as
EN te i=1 [StdDev({Score(oi, ôti)|Tt=1})] (8)
The sensitivity metrics here are the lower the better and are denoted by ↓ in the following sections.

heading: 4.2 Tasks and Datasets
text: We implement our experiments on the tasks below. The statistics are in Table 1 and we calculate the macro-average scores of samples for each metric.
Dialogue Summarization outputs fluent and concise summaries covering the salient information in dialogues. We experiment with the SAMSum dataset (Gliwa et al., 2019) consisting of around 16k open-domain dialogues among two or more interlocutors. Rouge-2 F1 (Lin, 2004) and BertScore F1 (Zhang et al., 2019)2 are task-specific evaluation
2We adopted microsoft/deberta-xlarge-mnli recommended by https://github.com/Tiiiger/bert_score for BertScore.
metrics. We consider genders to be consistent when switching names following Khalifa et al. (2021).
Question Generation is to generate a question given an input dialogue and its corresponding answer span. We use Molweni dataset (Li et al., 2020) made up of around 10k task-oriented dialogues sampled from the Ubuntu Chat Corpus. Similar to the question generation work based on SQuAD1.1, we extract (dialogue, answer, question) tuples from the original Molweni dataset and ignore unanswerable questions. Bleu (Papineni et al., 2002) and Rouge-L F1 are used for evaluations.
Reading Comprehension generates an answer by inputting a dialogue with a question. We use the Molweni dataset (Li et al., 2020) and ignore unanswerable questions as well. Bleu and Rouge-L F1 are also used for evaluations.

heading: 4.3 Implementation Details
text: We use BART-large as our basic pre-trained model. We truncate inputs to the first 1024 tokens and the learning rate is 3e− 5 with weight decay equaling 0.01. The model is fine-tuned with batch size equaling 32 for 10 epochs. We evaluate the performance on Dva after each epoch with Rouge-2 F1 or Bleu. The checkpoint with the highest score on Dva is saved for testing. During the inference, we decode with no_repeat_ngram_size=3, length_penalty=1.0 and num_beams=4. We search α and β in {1, 10, 20} empirically and report results with the best validation performance. Specifically, α equals 1. β equals 1 for reading comprehension and 10 for the others. Our experiments are done on a single RTX 2080Ti with 11G GPU memory. Considering the GPU memory footprint, we set K = 2, which is the same for Aug and FreAug for fair comparisons.
We test online approaches with their corresponding test sets. For offline approaches, we focus on two sources of P . One is in-distribution names representing speaker names from the corresponding Dtr. The other is all-possible names with more than 117 thousand names3, which can reflect the models’ performances in complicated real scenarios. For approaches with sampling operations, we construct data with 3 different random seeds. Results are averaged over the number of runs.

heading: 5 Results
text: We show performances of approaches first, followed by ablation studies and evaluations. Then,
3https://data.world/arunbabu/gender-by-names
we take a closer look at offline approaches, which show the inherent capability of models, with multifaceted analysis. Hyper-parameter search and case studies are in Appendix C and E.

heading: 5.1 Performance of Offline Approaches
text: The performance on the original test sets is shown in Table 2. Emb only outperforms Vanilla on question generation and Aug only makes little improvements over Vanilla on dialogue summarization. Our approach Ins makes consistent improvements, performing best among offline approaches.
Results with sensitivity scores are in Table 3. Emb fails to generate more insensitive results, especially for question generation. Aug doesn’t make promising improvements on outputs’ quality over Vanilla, but it reduces the sensitiveness of models across different test sets and tasks. Ins leads to better results on randomly augmented training data with different random seeds, significantly outperforming Aug. In a word, Ins achieves the best performance among offline approaches.
By comparing the results in Table 3 horizontally, in-distribution names perform better than all-possible names on dialogue summarization, whereas results are opposite on the others. Speaker names in SAMSum are mostly real and popular names, while names in Molweni are online nicknames containing unknown words, such as “zykotick9”. All-possible names contain a large proportion of real names, and a small proportion of names never seen during pre-training which can be regarded as nicknames. In this way, we can observe that the difficulty of modeling names for a model is “SAMSum in-distribution < all-possible < Molweni in-distribution”. In other words, models perform better on more popular names, which is in accord with the success of Fre in Sec. 5.2.

heading: 5.2 Performance of Online Approaches
text: The results of online approaches are in Table 4.
All speaker names will be normalized into fixed code names in ID, so that the test set for ID is changeless for each sample and the sensitivity scores are actually 0.0. Unfortunately, its quality scores lag behind Ins and even drop dramatically on dialogue summarization. Thus, it’s not recommended to be a necessary data pre-processing step.
Fre makes some improvements on R2 for dialogue summarization by comparing with the vanilla model, which is consistent with the results in (Khalifa et al., 2021), whereas the drops in BertScore were not mentioned in their work. The sensitivity scores are lower than those for offline approaches in Table 3. To better understand the gains of Fre, we further test the vanilla model with the same test sets replaced by frequent names. It achieves similar performance on Rouge-2 (28.18) and BertScore (75.13) with the vanilla model. The sensitivity score D-BertS is 2.24, which is lower than 2.49 of
Vanilla in Table 3. It shows that the advantages of Fre not only come from using the group of frequent names that are easier for a model to understand, but also from doing fine-tuning with this group of names. FreAug doesn’t improve the outputs’ quality consistently, but reduces the sensitivity scores.
FreIns performs the most insensitively with better generation quality among online approaches.

heading: 5.3 Ablation Study
text: Ablation studies of our full approach Ins are in Table 5. Aug is regarded as an ablation representing the model trained without any auxiliary losses. Both insensitivity losses outperform Aug with using Ldh topping the rank on most metrics, showing that penalizing differences on the decoder hidden states has more direct effects on the outputs. Combining both losses induces more performance gains.

heading: 5.4 Human Evaluation
text: Taking dialogue summarization as an example, we did human evaluation to further prove the improvement on sensitivity by sampling 200 pairs of generations for each offline approach and asked three proficient English speakers from Asia to label each
case out of 4 choices by selecting the primary one that makes the generations distinct: Information difference means both outputs contain different information or keywords. Factual difference refers to different matchings between speakers and events. Expression difference is that the outputs have minor differences, such as capitalization and different orders of juxtaposed names. Same represents the identical outputs. The results are in Fig. 2 with 0.64 Kappa score, indicating substantial agreement. We can see that content distinction is the primary difference type. Ins generates less distinct contents and more identical results, outperforming the baselines.

heading: 5.5 Sensitivity among Name Groups
text: We collect specific groups of names in terms of popularity and race and show differences in the quality performances on test sets constructed with corresponding names. The sensitivity among different groups for each method are reflected by the scattering of dots vertically in Fig. 3.
Name groups by popularity and usage: We define 4 groups. Frequent including words frequently and solely used as human names is mentioned before. Polysemous represents words frequently used but not specialized for human names, such as June and Florida. Rare is names with low occurrence times like Paderau. Unknown names are similar to random strings from a model’s perspective since they haven’t been exposed to the model. The last three groups are collected by counting occurrences of all-possible names in the pretraining corpus of BART. We select 200 names for each group (More details are in Appendix B).
According to Fig. 3a, we can see that models usually perform poorly on Polysemous, even worse than Rare and Unknown. The daily meanings dominate the representation of this word and confuse the model. Frequent generally outperforms other groups. We conclude that words frequently and uniquely used as names that result in more specialized embeddings in pre-trained models and perform
better. Moreover, comparing the sensitivity among different approaches, Ins outperforms the baselines in most cases except Aug. It achieves more centralized dots due to the performance reduction on the dominant groups or even all groups, showing that models tend to overfit with augmented data without our losses. To recap, Ins results in consistent improvements over Vanilla among different tasks compared with other baselines.
Name groups by races: Names from different races are from Tzioumis (2018) by assigning each name to a race with the highest probability. 4 major groups4 are gathered, including Non-Hispanic White, Hispanic or Latino, Non-Hispanic Black or African American, and Non-Hispanic Asian or Native Hawaiian or Other Pacific Islander. To avoid the influence of the various number of names, we select the most frequent 50 names in each group and show the results in Fig. 3b. All of the approaches show discrimination against Asian in dialogue summarization. Emb, Aug and Ins improve the insensitivity among different races compared with Vanilla, and Ins is better with the guarantee on quality. We consider to introduce special designs on demographic features in the future.
4Other groups are empty after this assigning operation with Tzioumis (2018)’s name list.

heading: 5.6 Sensitivity on an Individual Speaker
text: We can also only change the name of a single speaker each time to analyze fine-grained sensitivity. The results of offline approaches for dialogue summarization are shown in Table 6 (see more in Appendix D). The sensitivity scores are lower than the ones in Table 3. It seems that the sensitivity of models is proportional to the amount of changes in test samples, i.e., whether changing all speaker names (change-all-name) or only one speaker name (change-one-name). However, it’s not always true and changing one name can be more sensitive than changing all names. Taking the results from Ins as an example, around 52.01% samples have speakers whose change-one-name D-BertS is higher than the corresponding changelall-name one. Over 34.80% of the change-onename D-BertS averaged by speakers from the same dialogue is also higher than the change-all-name D-BertS.
We further show the trends between speaker features and their sensitivity scores in Fig. 4. Names are more sensitive and thus crucial for speakers at the start of a dialogue or with more utterances, deserving attention for further improvements.
1.6
1.9
2.2
1 2 3
D -B
er tS
(% )
Utterance Index of the First Appearance

heading: 6 Related Work
text: Entity/Name Bias in Narrative Texts: Previous work on entity biases shows that pre-trained lan-
guage models are sensitive to changes in narrative text. Some works (Zhang et al., 2018, 2017; Wang et al., 2022b) for relation extraction mask entities in the context to prohibit learning spurious features between entities and relations. Yan et al. (2022) analyzes the robustness of models by entity renaming on reading comprehension. They all consider different kinds of entities, such as person and organization. However, the entities have the potential to be grounded in real life (Smith and Williams, 2021), and the background knowledge of these entities may be necessary for understanding. Besides, the context and the entities cannot always be well-separated, especially persons Yan et al. (2022). Thus, masking and switching operations are not always suitable for these entities. In our work, we focus on speakers that are not grounded.
Names that are not grounded have also been studied. Information such as age, gender and race can be reflected by a given name to some extent (Girma, 2020), while models learned with statistical features may make wrong predictions about specific persons or bring unexpected stereotypes (Bertrand and Mullainathan, 2004). Romanov et al. (2019) takes occupation classification as an example and discourages the model to predict an individual’s occupation depending on his/her name. Wang et al. (2022a) presents that machine translation models perform poorly on female names when translating into languages with grammatical gender and also have sentiment bias caused by names with sentiment-ambiguous words. Samples in all these works only have a single name each, while multiple speaker names are entangled in a single dialogue.
Fairness of Dialogue Models: Safety and fairness issues on generations from dialogue models are crucial for implementation in practice. Harmful differences in responses caused by different demographic personas are observed in well-known dialogue systems (Sheng et al., 2021; Dinan et al., 2020), including offensiveness, gender bias, race discrimination, etc. These unfairness phenomena also exist in dialogue systems without considering persons (Liu et al., 2020), reflected by the politeness, sentiment, diversity and other aspects of a response. Recent work from (Smith and Williams, 2021) shows dialogue models treat their conversation partner differently for different speaker names. Instead of analyzing differences in open-ended dialogue systems, we target on text generation tasks given dialogues and show that sen-
sitivity/unfairness also exists among speakers.

heading: 7 Conclusion
text: This paper focuses on the speaker name sensitivity in the text generation from dialogues. We provide a classification for previous approaches, and propose the insensitivity losses to reduce the sensitivity while achieving favorable generation quality. Fair comparisons and comprehensive analysis are done among different approaches for evaluating the sensitivity quantitatively. More approaches targeting dialogue sensitivity issues are expected.
Limitations
Our work has the following limitations: First, we cannot generalize our conclusions to other languages that are dramatically different from English or more complicated multi-lingual scenarios without further experiments.
Second, we didn’t consider any special designs on demographic features of names in our proposed approach. As shown in Sec. 5.5, discrimination does exist among different groups. Although Ins outperforms other baselines overall, there is still room to improve insensitivity among different groups for tasks with longer outputs containing multiple speaker names. We hypothesize that demographic features of names can be added through a more dedicated data augmentation strategy.
Third, our experimentation was restricted to the BART model in this paper. The reason is that among all the models that can be fine-tuned with our limited resources, including T5 and GPT-2, BART is still the best and the most popular, therefore we pick BART as the target of this study. Our intention is to devote the limited paper space to a more in-depth analysis of the problem using a range of tasks. Besides, it should be noticed that the speaker name sensitivity is still an issue with recent large pre-trained models, as shown in the example of dialogue summarization with outputs from ChatGPT in Fig. 5. The two summaries are expected to be the same, modulo speaker names. However, the third speaker (Sergio/Ashley) is not even mentioned in Summary-2.
We will try to address these limitations in the future.
Ethics Statement
All of the name lists we adopted in this paper are borrowed from public websites (https://www.
ssa.gov) and previous publications (Tzioumis, 2018; Khalifa et al., 2021). We considered only binary genders and four different racial groups, which are clearly incomplete for depicting all humans. Our work is mainly at drawing researchers’ attention to the unfairness caused by speaker names in text generation tasks given dialogues. These demographic features are selected to shed light on this potential issue and our method is not restricted to any specific demographic groups.

heading: Acknowledgments
text: This work was generously supported by the CMB Credit Card Center & SJTU joint research grant, and Meituan-SJTU joint research grant.

heading: B Name Groups
text: To collect polysemous, rare and unknown names, we counted the number of occurrences of all-possible names in the pre-training corpus,
Wikipedia5 and BookCorpus6. We denote the frequency of a name as fexact and fner representing doing exact string match or named entity recognition when counting name occurrences respectively. Rare contains names shown at least once and with the lowest fexact not equaling 0. Unknown includes names with fexact equaling 0. According to our observations, we find that names with a larger fexact are likely to be polysemy and are not uniquely used as personal names. So, we design a metric to recognize such names as follows:
u = rank(fexact)− rank(fner) rank(fexact) + rank(fner)
(9)
rank(·) means that the ranking of a name among the whole name list based on its frequency in descending order 7. A higher u shows a higher level of uniqueness of a word as a name. The names with the lowest u scores are selected as Polysemous in Sec. 5.5.
Examples of names in different name groups are listed as follows:
• Frequent: Alexis, Philip, Matthew, Frank, Tyler, Roy, Catherine, Joan, Amanda, Henry
• Polysemous: July, Sea, March, Paris, Treasure, Oxford, Romania, Ice, Jersey, Navy
• Rare: Makinzy, Diyanna, Javione, Zamire, Harkeem, Jerralyn, Crissi, Monque, Ajahar, Dijion
• Unknown: Jaliyiah, Cardelia, Ravindr, Josephanthony, Tyjohn, Tnaya, Jyren, Kashaunda, Jaykob, Latonnia
• White: Kim, Georgia, Joseph, Mark, Martin, James, William, Barbara, Richard, Victoria
• Hispanic: Sofia, Daisy, Luis, Manuel, Dora, Emilia, Minerva, Antonio, Oscar, Francisco
• Black: Kenya, Ebony, Anderson, Kelvin, Dexter, Cleveland, Percy, Mamie, Jarvis, Essie
• Asian: Kong, Muhammad, Gang, Mai, Chi, Krishna, Can, Wan, Wang, Ferdinand
5https://huggingface.co/datasets/wikipedia 6https://huggingface.co/datasets/bookcorpus 7Doing named entity recognition on the whole pre-training corpus is too time-consuming. Therefore, we randomly sample 1% of the data for counting the fner and use the name rankings in Eq. 9 to get the uniqueness score.

heading: C Hyper-parameter Search
text: We empirically searched the hyper-parameters α and β in {1, 10, 20} respectively with 9 combinations for Ins. Due to the limited computation resources and the large search space, we trained the model with different combinations for a single time, selected the best 3 combinations and repeated experiments with different random seeds to determine the final choice of α and β according to the performance on Dva. Finally, we set (α, β) as (1, 10), (1, 10), (1,1) for dialogue summarization, question generation and reading comprehension respectively. We directly borrow these settings for FreIns.
In Fig. 7, we show the performances of Ins under different combinations for dialogue summarization on the vanilla test set with a single run. We can see that all of the results outperform the baselines in Table 2 and the standard deviation of BertScore among different combinations is only 0.14%, showing the stable improvements of Ins over the baselines.

heading: D Additional Results of Sensitivity on an Individual Speaker
text: Results for sensitivity on an individual speaker on all of the three tasks are in Table 7 and Table 8. Both tables lead to the same observations and con-
Dialogue-1
clusions as discussed in Sec 5.1 and Sec 5.2, where Ins and FreIns perform best among offline and online approaches correspondingly.

heading: E Case study
text: We show cases for different tasks in this section. The case for dialogue summarization is in Fig. 8. Vanilla extracts different information for two sets of names: “She will bring eggs” and “Ethie is off on Friday”. It also uses different expressions: “will come to ... for Easter” and “invited ... for Easter”. Besides, “Louise” is only mentioned in the second summary. Emb has the information difference and the expression difference. Meanwhile, it outputs incorrect content in the second summary, where “chocolat ones” is used for describing “eggs” in the input dialogue. Aug outputs more information for the first set of names. Ins treats the two sets of names equally with the same generations modulo the speaker names.
In the case of question generation in Fig. 9, all baselines generate “who gives Jernee suggestions?” for the second set of names, which is an inaccurate question with multiple candidate answers. Emb also generates a “Who” with the capitalized first
Dialogue-1
letter, which is also different from the other one with lowercase “who” if we compare them strictly. Ins generates identical and accurate questions for the same dialogue with different speaker names.
For reading comprehension in Fig. 10, both Vanilla and Emb generate quite different answers for two sets of names. Aug generates consistent but wrong answers considering the one-to-one mapping of speaker names. Ins outputs identical correct and complete answers, outperforming the baselines.
ACL 2023 Responsible NLP Checklist

heading: A For every submission:

heading: 3 A1. Did you describe the limitations of your work?
text: It is the section after the conclusion.
7 A2. Did you discuss any potential risks of your work? We include an Ethics Statement after the Limitations. Our work aims at reducing sensitivity on speaker names. In other words, we try to reduce potential risks of current models.

heading: 3 A3. Do the abstract and introduction summarize the paper’s main claims?
text: Section 1
7 A4. Have you used AI writing assistants when working on this paper? Left blank.
B 3 Did you use or create scientific artifacts? Section 4.2

heading: 3 B1. Did you cite the creators of artifacts you used?
text: Section 4.2
7 B2. Did you discuss the license or terms for use and / or distribution of any artifacts? All of the datasets are publicly available and we will only release the codes and results of our work.

heading: 3 B3. Did you discuss if your use of existing artifact(s) was consistent with their intended use, provided
text: that it was specified? For the artifacts you create, do you specify intended use and whether that is compatible with the original access conditions (in particular, derivatives of data accessed for research purposes should not be used outside of research contexts)? Section 3.1
B4. Did you discuss the steps taken to check whether the data that was collected / used contains any information that names or uniquely identifies individual people or offensive content, and the steps taken to protect / anonymize it? Not applicable. All of the datasets are publicly available and widely-used.
B5. Did you provide documentation of the artifacts, e.g., coverage of domains, languages, and linguistic phenomena, demographic groups represented, etc.? Not applicable. We are not a dataset paper. We provided necessary information about the dataset in Section 4.2 and Table 1. More details please refer to their original dataset paper.
3 B6. Did you report relevant statistics like the number of examples, details of train / test / dev splits, etc. for the data that you used / created? Even for commonly-used benchmark datasets, include the number of examples in train / validation / test splits, as these provide necessary context for a reader to understand experimental results. For example, small differences in accuracy on large test sets may be significant, while on small test sets they may not be. Section 4.2, Table 1
C 3 Did you run computational experiments? Section 5

heading: 3 C1. Did you report the number of parameters in the models used, the total computational budget
text: (e.g., GPU hours), and computing infrastructure used? Section 4.3
The Responsible NLP Checklist used at ACL 2023 is adopted from NAACL 2022, with the addition of a question on AI writing assistance.

heading: 3 C2. Did you discuss the experimental setup, including hyperparameter search and best-found
text: hyperparameter values? Section 4.3, Appendix C

heading: 3 C3. Did you report descriptive statistics about your results (e.g., error bars around results, summary
text: statistics from sets of experiments), and is it transparent whether you are reporting the max, mean, etc. or just a single run? Section 4.3

heading: 3 C4. If you used existing packages (e.g., for preprocessing, for normalization, or for evaluation), did
text: you report the implementation, model, and parameter settings used (e.g., NLTK, Spacy, ROUGE, etc.)? Section 4.2. We followed the previous work on task-specific evaluation metrics and will release the corresponding codes.
D 3 Did you use human annotators (e.g., crowdworkers) or research with human participants? Section 5.4

heading: 3 D1. Did you report the full text of instructions given to participants, including e.g., screenshots,
text: disclaimers of any risks to participants or annotators, etc.? Section 5.4
D2. Did you report information about how you recruited (e.g., crowdsourcing platform, students) and paid participants, and discuss if such payment is adequate given the participants’ demographic (e.g., country of residence)? Not applicable. We had student volunteers to do the human evaluation.
D3. Did you discuss whether and how consent was obtained from people whose data you’re using/curating? For example, if you collected data via crowdsourcing, did your instructions to crowdworkers explain how the data would be used? Not applicable. The volunteers knew how the data would be used before doing the human evaluation.
D4. Was the data collection protocol approved (or determined exempt) by an ethics review board? Not applicable. We did not collect new datasets, only a simple human evaluation.

heading: 3 D5. Did you report the basic demographic and geographic characteristics of the annotator population that is the source of the data?
text: Section 5.4.

heading: Future Work
text: We will try to address these limitations in the future