SlowGuess/NAACL_2022_OCR huggingface dataset:
Figure 2: Proposed approach for Lifelong Intent Detection, where a PLM is jointly fine-tuned on (A) Intent detection, for generating the "intent label" corresponding to a user utterance (B) Generative replay, i.e., given a prompt consisting of the "intent" and a representative set of words from that intent, generate a user utterance along with the corresponding "intent label"(C) Supervised contrastive learning, i.e., given a valid utterance intent pair  $(U,I_1)$  and another randomly chosen intent  $I_{2}$ , predict whether  $I_{1}$  and  $I_{2}$  correspond to the same intent. For E.g., ("How do I link my new card ?","Card Linking") is an example of an (utterance,intent) pair and "Cash Withdrawal Charge" refers to a (possibly different) intent.
Figure 3: Answered Prompts: (A) Prompt without a question  $(PWQ)$ , (B) Prompt with an incremental question  $(PWIQ)$ , (C) Prompt for Labelled Utterance Generation  $(LUG)$
words from  $C_j$  for every intent along with  $y_j$  and generates pseudo-labelled utterances corresponding to each entry in memory.
$$
L _ {R} = - \left(\log \left(p (x, A N S, y, E O S \mid y, c _ {j k}, S E P; \varphi)\right)\right) \tag {5}
$$
5.3 Supervised Contrastive Training (SCT)
To minimize the likelihood of incorrect utterance-label pairs during  $LID$  and  $LUG$ , we explicitly fine-tune  $\mathcal{TL}_{i+1}$  on positive and negative utterance-label pairs via contrastive loss as shown in Eq. 6. A pair  $((x,y),y')$  where  $y' \in Y_{all}$  is positive if  $y = y'$  or negative otherwise.  $p_{ul} = \text{sigmoid}(W_c h_{EOS} + b_c)$ , represents the probability that  $((x,y),y')$  is positive and  $h_{EOS}$  is a representation of  $EOS$  token from the PLM.  $W_c$  and  $b_c$  represent the weight and bias of a linear layer respectively.
$$
L _ {S C T} = p _ {u l} * \log \left(p _ {u l}\right) + \left(1 - p _ {u l}\right) * \log \left(1 - p _ {u l}\right) \tag {6}
$$
5.4 Knowledge Distillation (KD)
To alleviate catastrophic forgetting, we also use  $KD$  (Hinton et al., 2015) in the context of  $LID$  where instead of hard targets we use soft targets to train  $\mathcal{IL}_{i+1}$  on  $S^i$ . For intent detection, we distill the knowledge from  $\mathcal{IL}_i$  while training  $\mathcal{IL}_{i+1}$  by minimizing the loss function in Eq. 7 where  $p'(y_{s+1})$
is obtained from  $\mathcal{TL}_i$  and  $y_0 = ANS$ .
$$
L _ {K D} = - \frac {1}{N} \sum_ {s = 0} ^ {N} p ^ {\prime} \left(y _ {s + 1}\right) * \tag {7}
$$
$$
l o g (p (y _ {s + 1} \mid x, y _ {0}, \dots , y _ {s}; \varphi))
$$
At the current incremental step, we incrementally update  $\mathcal{L}_i$  based on  $\mathcal{D}_{train}^{i+1} \cup \mathcal{S}^i$  by minimizing  $L_{total}$  as shown in Eq. 8 to get  $\mathcal{L}_{i+1}$ .
$$
L _ {t o t a l} = \lambda_ {1} * L _ {I D} + \lambda_ {1} * L _ {K D} + \lambda_ {2} * L _ {R} + \lambda_ {3} * L _ {S C T} \tag {8}
$$
where  $\lambda_1, \lambda_2, \lambda_3$  are the set of hyperparameters. At inference, we use  $\mathcal{IL}_{i+1}$  to generate intent labels for all utterances in  $\mathcal{D}_{test-full}^{i+1}$ .
We define  $PAGeR$ -Base as a baseline for  $PAGeR$ , which extends LAMOL for Class-IL. We train  $PAGeR$ -Base on  $\lambda_1 * L_{ID} + \lambda_2 * L_R$  as our loss function where replay samples are generated based on the  $PWQ$  prompt.
In this section, we describe the various datasets, baseline approaches, evaluation metrics and training details. We subsequently present the results and discuss our key observations. For further training details, please refer to A.2 in appendix.
6.1 Dataset Description
Lifelong Intent Detection: CLINc150 (Larson et al., 2019) is a crowdsourced multi-domain (10
domains such as utility, travel etc.) intent detection dataset. HWU64 (Liu et al., 2019) is a crowdsourced multi-domain (21 domains such as alarm, cooking etc.) intent detection dataset. BANKING77 (Casanueva et al., 2020) is an imbalanced, fine-grained intent detection dataset from the banking domain. SGD (Rastogi et al., 2020) and MWOZ (Budzianowski et al., 2018) are multidomain dialogue datasets which consist of dialogues from 19 and 8 domains respectively.
Text Classification: Stackoverflow (S20) (Xu et al., 2017) is a multi-class classification dataset comprising of 20,000 question titles from Stackoverflow each tagged with one of 20 different tags. Lifelong Relation Extraction: FewRel (Han et al., 2018): is a relation extraction dataset where the objective is to predict a relation type between a pair of entities in a given sentence/paragraph.
For all datasets, we use standard train-test split provided by respective authors (shown in Table 5). For further dataset details, please refer to A.1 in appendix.
6.2 Baseline Approaches
We use Upper Bound (UB) where we assume that data corresponding to all incremental steps is available apriori and fine-tune  $\mathcal{L}$  on all  $\mathcal{D}^i$ 's together and in Lower Bound (LB), we incrementally fine-tune  $\mathcal{L}$  on  $\mathcal{D}^i$ 's with  $|\mathcal{M}| = 0$ .
6.2.1 Lifelong Intent Detection
(i) EWC (Kirkpatrick et al., 2017): Remembers old tasks by selectively slowing down learning on the weights important for these tasks.
(ii) A-GEM (Chaudhry et al., 2019a): A-GEM tries to ensure that at every training step the average episodic memory loss over the previous tasks does not increase.
(iii) ER (Rolnick et al., 2019): Interleaving old samples with current data in training batches.
(iv) DER (Buzzega et al., 2020): Rehearsal with knowledge distillation and regularization.
(v) AdapterCL (Madotto et al., 2021): Train a task-specific residual adapters (Houlsby et al., 2019) and uses an entropy-based classifier to select which adapter to use at test time.
6.2.2 Lifelong Relation Extraction
(i) GEM (Lopez-Paz and Ranzato, 2017): GEM ensures that at every training step the loss for each of the previous tasks, approximated by the samples in episodic memory, does not increase.
(ii) EMR (Parisi et al., 2019): Jointly train a model on current and old labelled samples stored in memory.
(iii) EA-EMR (Wang et al., 2019): They utilize an explicit alignment model to mitigate the sentence embedding distortion of the learned model when training on new data and new relations.
(iv) EMAR (Han et al., 2020): Every time neural models are activated to learn both new and memorized data, EMAR utilizes relation prototypes as a memory reconsolidation exercise to keep a stable understanding of old relations.
We use the following two metrics for evaluating performance on the lifelong intent detection task.
Average Accuracy (Chaudhry et al., 2018) We use  $Avg_{acc}^{k'}$  to evaluate the performance after  $k'$  incremental steps where  $Acc_{avg}^{k'} = \frac{1}{k'}\sum_{i=1}^{k'} A(\mathcal{IL}_{k'}(\mathcal{D}_{test}^i))$  and  $A(\mathcal{IL}_{k'}(\mathcal{D}_{test}^i))$  represents classification accuracy obtained by  $IL_{k'}$  after  $k'$  incremental steps on  $\mathcal{D}_{test}^i$ .
Average Forgetting (F) (Chaudhry et al., 2018) We use  $F_{avg}^{k'}$  to measure the average drop in intent classification accuracy for intents introduced after  $k'$  incremental steps where  $F_{avg}^{k'} = \frac{1}{k'} \sum_{i=1}^{k'-1} \max_{1 \leq t < k'} A(\mathcal{IL}_t(\mathcal{D}_{test}^i)) - A(\mathcal{IL}_{k'}(\mathcal{D}_{test}^i))$  and  $F_{avg}^{k'} \in [-1,1]$  (lower value of  $F_{avg}^{k'}$  implies less forgetting).
For lifelong intent detection task, we compare  $PAGeR$  with SOTA approaches for Class-IL from the NLP and vision community on three imbalanced and one balanced intent detection dataset. The comparison was across three different sizes for  $\mathcal{M}$ . For a given  $\mathcal{M}$ , we run all baselines, including  $PAGeR$ -Base and  $PAGeR$  over four different intent permutations and report  $Acc_{avg}^{\mathcal{T}}$  and  $F_{avg}^{\mathcal{T}}$ . As shown in Table 1,  $PAGeR$  outperforms the baselines for all possible dataset- $\mathcal{M}$  combinations. In low memory scenarios, i.e.,  $\mathcal{M} = 1\%$ ,  $PAGeR$  outperforms exemplar replay based baselines by high margins which indicates that while it's not possible to store all representative samples in a given memory, these may be generated based on concept words. Similar to (Buzzega et al., 2020), we also find EWC and A-GEM less effective in the Class-IL setting. With  $\mathcal{M} = 1\%$ ,  $PAGeR$  also outperforms AdapterCL on all datasets except  $C150^I$ . We also compare  $PAGeR$  with MSR (Liu et al., 2021b) and
Intent Detection	TC
M	Approach	C150B	C150T	B77	HWU64	S20
UB	95.7	94.6	91.9	90.3	90.8
LB	36.9/0.66	30.1/0.75	23.3/0.88	24.4/0.85	21.6/0.94
EWC	36.9/0.66	31.6/0.74	21.1/0.89	23.8/0.86	20.8/0.94
AdapterCL	88.1/0.02	79.9/0.07	77.7/0.07	77.6/0.08	82.5/0.06
10%	A-GEM	30.8/0.71	13.6/0.92	17.3/0.93	18.8/0.87	67.6/0.3
ER	88.9/0.06	81.7/0.15	84.7/0.13	78.3/0.17	85.2/0.11
DER	90.8/0.03	83.9/0.1	80.7/0.15	81.3/0.12	85.6/0.11
PAGeR-Base (LAMOL)	92.7/0.04	89.7/0.07	82.9/0.13	84.0/0.123	72.5/0.28
PAGeR	94.9/0.01	93.4/0.02	90.2/0.02	89.9/0.04	88.7/0.05
5%	A-GEM	30.8/0.7	13.4/0.92	19.2/0.91	18.4/0.89	68.2/0.30
ER	85.2/0.1	71.1/0.28	76.5/0.21	72.0/0.25	82.6/0.15
DER	88.1/0.07	78.1/0.18	73.9/0.19	76.5/0.19	83.9/0.13
PAGeR-Base (LAMOL)	92.0/0.04	87.5/0.09	81.1/0.16	82.2/0.15	71.3/0.30
PAGeR	94.4/0.02	92.9/0.03	89.2/0.04	88.0/0.06	88.0/0.07
1%	A-GEM	28.7/0.73	13.9/0.91	25.3/0.83	17.9/0.89	62.4/0.4
ER	64.8/0.34	41.3/0.62	51.6/0.53	46.2/0.56	76.3/0.22
DER	73.0/0.24	45.9/0.56	53.0/0.5	49.4/0.51	77.4/0.22
PAGeR-Base (LAMOL)	86.9/0.10	63.6/0.37	68.1/0.32	70.5/0.30	68.3/0.34
PAGeR	92.4/0.04	76.4/0.22	80.1/0.17	79.2/0.19	84.3/0.13
Table 1: Average Accuracy (↑) / Average Forgetting (↓) on four intent detection dataset,  $\mathbf{C150}^B$ ,  $\mathbf{C150}^I$ : Balanced and Imbalanced version of CLINC150, BANKING77 (B77), HWU64 and on one Text Classification (TC) dataset.
show the results in table 8.
$PAGeR$  also outperforms other baselines for incremental text classification on the S20 dataset. Even with  $\mathcal{M} = 1\%$ ,  $PAGeR$  gets an average accuracy,  $Acc_{avg}^{\mathcal{T}}$  of  $84.3\%$  which is only  $6.5\%$  below the UB. For S20, we use numeric label ids as class labels to demonstrate that  $PAGeR$  also works when labels are not informative.
7.1 PWQ Vs PWIQ
As shown in Fig. 4, the length of  $PWIQ$  prompt depends on the number of intent labels which increases at every incremental step and leads to large sequence length whereas the length of  $PWQ$  prompt is independent of the number of intent labels. And large sequence length leads to more running time. Hence,  $PWQ$  prompt can be extended to operate over a large number of incremental steps.
Figure 4: PWQ Vs PWIQ
7.2 How good are pseudo-labelled utterances?
To alleviate catastrophic forgetting in the  $LID$  and  $LUG$  tasks, we use pseudo-labelled utterances for generative replay. To empirically assess the quality of pseudo-labelled utterances over incremental steps, we perform the following experiment: We only consider intents that have been introduced at the first incremental step. On these intents, we
train an intent detection model at each incremental step where we use pseudo labelled utterances generated by  $\mathcal{TL}_i$  at  $i^{th}$  incremental step and compare it with the model trained on real labelled-utterances (Upper Bound). As shown in Fig. 5, on the B77 dataset, the drop in average accuracy is not significant for these intents over incremental steps. This suggests that pseudo-labelled utterances generated by the model are close to original utterances, i.e., generative replay plays an important role towards alleviating catastrophic forgetting in both LID and LUG tasks.
Figure 5: Accuracy for intents introduced during first incremental step at subsequent incremental steps
8 Ablation Study
We conduct ablation experiments on the B77 dataset across all memory sizes to demonstrate the efficacy of  $PAGeR$  's sub-components as shown in Table 2. Individually, all three components when applied with  $PAGeR$ -Base, show significant improvement in performance  $(Acc_{avg}^{\mathcal{T}})$ . LUG, when applied with  $PAGeR$ -Base to generate better representative samples for replay, boosted the  $Acc_{avg}^{\mathcal{T}}$  of  $PAGeR$ -Base by  $4.7\%$ ,  $4.9\%$  and  $5.7\%$  in experiments where  $\mathcal{M}$  is set to  $10\%$ ,  $5\%$  and  $1\%$  respectively. Applying SCT with  $PAGeR$ -Base also shows significant improvement in experiments
across different sizes for  $\mathcal{M}$  Results of  $PAGeR$ -Base with KD only, affects the  $Acc_{avg}^{\mathcal{T}}$  of  $PAGeR$ -Base by  $-0.5\%$ $-0.4\%$  and  $+1.0\%$  in experiments when  $\mathcal{M}$  is  $10\%$ ,  $5\%$  and  $1\%$  respectively suggesting that usage of KD by itself is not effective in alleviating catastrophic forgetting.
In row  $PAGeR$  w/o LUG, removing LUG from the combined approach highlights the importance of LUG and how the other two components, SCT and KD, are complementary to each other. This is reflected in the improved performance of  $PAGeR$ -Base by  $6.8\%$ ,  $7.6\%$  and  $10.4\%$  in experiments with  $\mathcal{M}$  set to  $10\%$ ,  $5\%$  and  $1\%$  respectively. A similar trend is proven by rows  $PAGeR$  w/o SCT,  $PAGeR$  w/o KD, suggesting how all of these components are complementary to each other. In case of small memory size ( $\mathcal{M} = 1\%$ ), all three components are required to achieve good intent detection accuracy, i.e., there is a drop of  $1.5\%$ ,  $3.4\%$ , and  $5.3\%$  if we remove LUG, SCT and KD from  $PAGeR$ .
When all components are applied together with PAGeR-Base to give PAGeR, the  $Acc_{avg}^{\mathcal{T}}$  improves from  $82.9\%$  to  $90.2\%$ ,  $81.1\%$  to  $89.2\%$ , and  $68.1\%$  to  $80.1\%$  with  $\mathcal{M}$  set to  $10\%$ ,  $5\%$  and  $1\%$  respectively.
Approach	M=10%	M=5%	M=1%
PAGeR-Base (LAMOL) (A)	82.9	81.1	68.1
A w/ LUG only	87.6	86.0	73.8
A w/ SCT only	87.9	86.2	72.1
A w/ KD only	82.4	80.7	69.1
B w/o LUG	89.7	88.7	78.5
B w/o SCT	88.8	88.0	76.7
B w/o KD	90.0	87.7	74.8
PAGeR (B)	90.2	89.2	80.1
9 Applications of PAGeR
Table 2: Ablation on components of PAGeR on BANKING77 dataset. In  $PAGeR$ -Base (LAMOL), generative replay is based on only intent labels whereas in  $PAGeR$ -Base (LAMOL) + LUG, we use intent specific concept words along with intent labels for generative replay.
M	Approach	FewRel
50 labelled samples per incremental step	UB	92.7
LB	66.2
EWC	30.2
GEM	59.8
A-GEM	47.5
EMR	65.1
EA-EMR	69.9
EMAR	77.9
PAGeR	91.3
9.1 Lifelong Relation Extraction
We applied  $PAGeR$  on a lifelong relation extraction task with the same experimental setup as described in Han et al. (2020), where the objective is to identify the correct relation mentioned in a given sentence from a set of candidate relations. To utilize candidate relations in  $PAGeR$ , we use  $PWID$  instead of  $PWD$  and replace  $User Utterance$ ,  $Incremental Question$  and  $Intent$  with sentence, candidate relations and correct relation respectively as outlined in Fig. 3 (B).  $PAGeR$  outperforms the state-of-the-art baseline  $EMAR$  by  $13.4\%$  in terms of  $Acc_{avg}^{\mathcal{T}}$  and is only  $1.4\%$  below the  $UB$  with  $\mathcal{M} = 50$  per incremental step, as shown in Table 3. These results suggest that  $PAGeR$  can be extended to other class incremental NLP problems.
9.2 Multi-domain Dialog
We also evaluate  $PAGeR$  in a scenario where new intents are added as part of new domain at each incremental step and we use  $K$  previous utterances along with current utterance for intent detection. We use  $SGD$ ,  $MWOZ$  multi-domain dialogue datasets and compare  $PAGeR$  with AdapterCL. As shown in Table 4, in this scenario,  $PAGeR$  outperforms AdapterCL.
Table 3: Average Accuracy on Lifelong relation extraction task. Except UB and LB, we have taken all baseline results from (Han et al., 2020)
SGD	MWOZ
Approach	K=0	K=1	K=0	K=1
AdapterCL	65.0	77.0	94.5	96.51
PAGeR	77.12	92.6	98.10	96.0
Table 4: Average Accuracy on incremental Multidomain intent detection task
The current paper addresses the problem of lifelong intent detection and proposes prompt augmented generative replay (PAGeR) to alleviate catastrophic forgetting. We model lifelong intent detection as a Class-IL problem and use a common PLM for intent detection and pseudo-labelled data generation which is jointly trained for both tasks yielding SOTA performance. Future work would involve demonstrating the versatility of PAGeR on a variety of different NLP tasks, and to apply it to real world Class-IL scenarios wherein the initial number of classes is large and relatively few intents are added or removed in each iteration with significant class imbalance.
Magdalena Biesialska, Katarzyna Biesialska, and Marta R. Costa-jussa. 2020. Continual lifelong learning in natural language processing: A survey. In Proceedings of the 28th International Conference on Computational Linguistics, pages 6523-6541, Barcelona, Spain (Online). International Committee on Computational Linguistics.
Paweł Budzianowski, Tsung-Hsien Wen, Bo-Hsiang Tseng, Iñigo Casanueva, Stefan Ultes, Osman Ramadan, and Milica Gašić. 2018. MultiWOZ - a large-scale multi-domain Wizard-of-Oz dataset for task-oriented dialogue modelling. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 5016-5026, Brussels, Belgium. Association for Computational Linguistics.
Pietro Buzzega, Matteo Boschini, Angelo Porrello, Davide Abati, and SIMONE CALDERARA. 2020. Dark experience for general continual learning: a strong, simple baseline. In Advances in Neural Information Processing Systems, volume 33, pages 15920-15930. Curran Associates, Inc.
Yue Cao, Hao-Ran Wei, Boxing Chen, and Xiaojun Wan. 2021. Continual learning for neural machine translation. In Proceedings of the 2021 Conference of the North American Chapter of the Association for Computational Linguistics: Human Language Technologies, pages 3964-3974, Online. Association for Computational Linguistics.
Inigo Casanueva, Tadas Temcinas, Daniela Gerz, Matthew Henderson, and Ivan Vulic. 2020. Efficient intent detection with dual sentence encoders. In Proceedings of the 2nd Workshop on NLP for ConvAI - ACL 2020. Data available at https://github.com/PolyAI-LDN/task-specific-datasets.
Arslan Chaudhry, Puneet K. Dokania, Thalaiyasingam Ajanthan, and Philip H. S. Torr. 2018. Riemannian walk for incremental learning: Understanding forgetting and intransigence. In The European Conference on Computer Vision (ECCV).
Arslan Chaudhry, Marc'Aurelio Ranzato, Marcus Rohrbach, and Mohamed Elhoseiny. 2019a. Efficient lifelong learning with a-gem. In ICLR.
Arslan Chaudhry, Marcus Rohrbach, Mohamed Elhoseiny, Thalaiyasingam Ajanthan, Puneet K Dokania, Philip HS Torr, and Marc'Aurelio Ranzato. 2019b. Continual learning with tiny episodic memories. arXiv preprint arXiv:1902.10486, 2019.
Ting Chen, Simon Kornblith, Mohammad Norouzi, and Geoffrey Hinton. 2020. A simple framework for contrastive learning of visual representations. In Proceedings of the 37th International Conference on Machine Learning, volume 119 of Proceedings of Machine Learning Research, pages 1597-1607. PMLR.
Yung-Sung Chuang, Shang-Yu Su, and Yun-Nung Chen. 2020. Lifelong language knowledge distillation. In Proceedings of the 2020 Conference on Empirical Methods in Natural Language Processing (EMNLP), pages 2914-2924, Online. Association for Computational Linguistics.
Cyprien de Masson d'Autume, Sebastian Ruder, Lingpeng Kong, and Dani Yogatama. 2019. Episodic memory in lifelong language learning. In Advances in Neural Information Processing Systems, volume 32. Curran Associates, Inc.
Beliz Gunel, Jingfei Du, Alexis Conneau, and Veselin Stoyanov. 2021. Supervised contrastive learning for pre-trained language model fine-tuning. In International Conference on Learning Representations.
Xu Han, Yi Dai, Tianyu Gao, Yankai Lin, Zhiyuan Liu, Peng Li, Maosong Sun, and Jie Zhou. 2020. Continual relation learning via episodic memory activation and reconsolidation. In Proceedings of the 58th Annual Meeting of the Association for Computational Linguistics, pages 6429-6440, Online. Association for Computational Linguistics.
Xu Han, Hao Zhu, Pengfei Yu, Ziyun Wang, Yuan Yao, Zhiyuan Liu, and Maosong Sun. 2018. FewRel: A large-scale supervised few-shot relation classification dataset with state-of-the-art evaluation. In Proceedings of the 2018 Conference on Empirical Methods in Natural Language Processing, pages 4803-4809, Brussels, Belgium. Association for Computational Linguistics.
Geoffrey E. Hinton, Oriol Vinyals, and J. Dean. 2015. Distilling the knowledge in a neural network. *ArXiv*, abs/1503.02531.
Ari Holtzman, Jan Buys, Li Du, Maxwell Forbes, and Yejin Choi. 2020. The curious case of neural text degeneration. In International Conference on Learning Representations.
Neil Houlsby, Andrei Giurgiu, Stanislaw Jastrzebski, Bruna Morrone, Quentin De Laroussilhe, Andrea Gesmundo, Mona Attariyan, and Sylvain Gelly. 2019. Parameter-efficient transfer learning for NLP. In Proceedings of the 36th International Conference on Machine Learning, volume 97 of Proceedings of Machine Learning Research, pages 2790-2799. PMLR.
Prannay Khosla, Piotr Teterwak, Chen Wang, Aaron Sarna, Yonglong Tian, Phillip Isola, Aaron Maschinot, Ce Liu, and Dilip Krishnan. 2020. Supervised contrastive learning. In Advances in Neural Information Processing Systems, volume 33, pages 18661-18673. Curran Associates, Inc.
Diederik P. Kingma and Jimmy Ba. 2014. Adam: A method for stochastic optimization. CoRR.
James Kirkpatrick, Razvan Pascanu, Neil Rabinowitz, Joel Veness, Guillaume Desjardins, Andrei A. Rusu,
Kieran Milan, John Quan, Tiago Ramalho, Agnieszka Grabska-Barwinska, Demis Hassabis, Claudia Clopath, Dharshan Kumaran, and Raia Hadsell. 2017. Overcoming catastrophic forgetting in neural networks. Proceedings of the National Academy of Sciences, 114(13):3521-3526.
Stefan Larson, Anish Mahendran, Joseph J. Peper, Christopher Clarke, Andrew Lee, Parker Hill, Jonathan K. Kummerfeld, Kevin Leach, Michael A. Laurenzano, Lingjia Tang, and Jason Mars. 2019. An evaluation dataset for intent classification and out-of-scope prediction. In Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP).
Pengfei Liu, Weizhe Yuan, Jinlan Fu, Zhengbao Jiang, Hiroaki Hayashi, and Graham Neubig. 2021a. Pretrain, prompt, and predict: A systematic survey of prompting methods in natural language processing. ArXiv, abs/2107.13586.
Qingbin Liu, Xiaoyan Yu, Shizhu He, Kang Liu, and Jun Zhao. 2021b. Lifelong intent detection via multi-strategy rebalancing.
X. Liu, Arash Eshghi, P. Swietojanski, and Verena Rieser. 2019. Benchmarking natural language understanding services for building conversational agents. In IWSDS.
David Lopez-Paz and Marc' Aurelio Ranzato. 2017. Gradient episodic memory for continual learning. In Advances in Neural Information Processing Systems, volume 30. Curran Associates, Inc.
Andrea Madotto, Zhaojiang Lin, Zhenpeng Zhou, Seungwhan Moon, Paul Crook, Bing Liu, Zhou Yu, Eunjoon Cho, Pascale Fung, and Zhiguang Wang. 2021. Continual learning in task-oriented dialogue systems. In Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing, pages 7452-7467, Online and Punta Cana, Dominican Republic. Association for Computational Linguistics.
Bryan McCann, Nitish Shirish Keskar, Caiming Xiong, and Richard Socher. 2018. The natural language decathlon: Multitask learning as question answering. arXiv preprint arXiv:1806.08730.
Michael McCloskey and Neal J. Cohen. 1989. Catastrophic interference in connectionist networks: The sequential learning problem. volume 24 of Psychology of Learning and Motivation, pages 109-165. Academic Press.
Natawut Monaikul, Giuseppe Castellucci, Simone Filice, and Oleg Rokhlenko. 2021. Continual learning for named entity recognition. Proceedings of the AAAI Conference on Artificial Intelligence, 35(15):13570-13577.
Abiola Obamuyide and Andreas Vlachos. 2019. Meta-learning improves lifelong relation extraction. In Proceedings of the 4th Workshop on Representation