SlowGuess/NAACL_2022_OCR huggingface dataset:
Table 11: Performance of DSSCC (Ours) on Intent Detection and Intent Discovery
KIR	Approach	ACC	NMI	ARI	ACC	NMI	ARI
0%	K-meansBERT	45.06	70.89	26.86	29.55	54.57	12.18
K-meansSBERT	61.04	82.22	48.56	55.72	74.68	42.77
AG	44.03	73.07	27.70	31.58	57.07	13.31
SAE-KM	46.75	73.13	29.95	38.92	63.79	22.85
DEC	46.89	74.83	27.46	41.29	67.78	27.21
DCN	49.29	75.66	31.15	41.99	67.54	26.81
DAC	55.94	78.40	40.49	27.41	47.35	14.24
DeepCluster	35.70	65.58	19.11	20.69	41.77	8.95
SCCL	33.52	66.63	18.89	13.41	34.14	4.02
25%	PCK-means	54.51	68.71	35.38	32.66	48.22	16.24
BERT-KCL	24.72	65.74	17.97	22.11	52.42	15.75
BERT-MCL	24.35	65.06	16.82	22.07	51.96	13.94
BERT-DTC	49.1	74.17	33.05	25.24	48.58	13.32
CDAC+	64.64	84.25	50.35	48.71	69.78	35.09
DeepAligned	73.71	88.71	64.27	48.88	70.45	36.81
DSSCC-KM	74.98	89.19	65.75	55.52	72.73	42.11
DSSCC-CH	75.72	89.12	66.72	48.38	66.39	33.94
DSSCC	75.72	89.12	66.72	55.52	72.73	42.11
50%	PCK-means	54.51	68.62	35.23	32.26	48.11	16.02
BERT-KCL	46.91	78.45	37.94	40.97	65.22	30.03
BERT-MCL	47.21	78.39	36.72	41.43	65.68	28.87
BERT-DTC	71.68	86.20	59.62	53.59	71.40	40.65
CDAC+	69.02	86.18	54.15	53.34	71.53	40.42
DeepAligned	80.22	91.63	72.34	59.23	76.52	47.82
DSSCC-KM	79.85	91.44	73.48	63.08	77.60	50.64
DSSCC-CH	81.46	91.39	73.48	59.14	73.11	44.98
DSSCC	81.46	91.39	73.48	63.08	77.60	50.64
75%	PCK-means	54.61	68.70	35.40	32.66	48.22	16.24
BERT-KCL	68.86	86.82	58.79	60.15	75.21	46.72
BERT-MCL	69.66	87.72	59.92	61.14	75.68	47.43
BERT-DTC	80.73	90.41	70.92	56.51	76.55	44.70
CDAC+	69.89	86.65	54.33	53.83	72.25	40.97
DeepAligned	86.01	94.03	79.82	64.90	79.56	53.64
DSSCC-KM	84.99	93.43	78.35	69.82	81.24	58.09
DSSCC-CH	87.91	93.87	81.09	68.13	78.15	54.23
DSSCC	87.91	93.87	81.09	69.82	81.24	58.09
Table 12: We report ACC, NMI and ARI on CLINC150 and BANKING77 datasets in the semi-supervised scenario for three different known intent ratios (KIR). Except  $\mathbf{K}$ -means $_{SBERT}$  and SCCL, we take all baseline results from Zhang et al. (2021c). For fair comparison we use  $BERT$  as PLM in DSSCC.
SNIPS	StackOverflow	DBPedia
KIR	Approach	ACC	NMI	ARI	ACC	NMI	ARI	ACC	NMI	ARI
25%	\( \mathbf{D_{ABERT}} \)	86.21	80.42	74.70	69.66	70.23	53.69	85.89	88.98	79.22
\( \mathbf{D_{SBERT}} \)	81.16	77.33	68.38	72.64	73.43	58.30	83.70	85.40	75.67
DSSCC-KM	93.77	89.05	86.78	79.37	76.93	58.73	89.44	89.25	83.29
DSSCC-CH	94.33	89.30	87.90	81.72	76.57	68.0	87.70	88.12	81.25
DSSCC	94.33	89.30	87.90	81.72	76.57	68.0	89.44	89.25	83.29
50%	\( \mathbf{D_{ABERT}} \)	85.69	83.03	77.03	72.89	74.49	57.96	88.63	91.24	83.38
\( \mathbf{D_{SBERT}} \)	88.83	84.19	79.53	73.07	74.08	59.34	87.29	88.80	81.04
DSSCC-KM	94.57	90.23	88.47	80.24	77.80	61.08	91.83	92.27	87.85
DSSCC-CH	95.20	91.07	89.67	82.43	77.30	68.94	92.14	92.70	88.61
\( \mathbf{D_{SSCC_{SBERT}}}\)	95.20	91.07	89.67	82.43	77.30	68.94	92.14	92.70	88.61
75%	\( \mathbf{D_{ABERT}} \)	90.10	86.94	82.42	74.51	76.24	59.45	92.17	93.25	88.12
\( \mathbf{D_{SBERT}} \)	92.70	88.22	85.40	75.50	75.90	61.21	91.17	91.14	85.94
DSSCC-KM	94.03	89.20	87.40	80.53	77.25	63.70	92.73	92.58	88.55
DSSCC-CH	94.87	90.44	89.03	82.65	77.08	68.67	92.13	92.61	88.65
DSSCC	94.87	90.44	89.03	82.65	77.08	68.67	92.73	92.58	88.55
Table 13: Intent detection and discovery results on three datasets, i.e., SNIPS, StackOverflow and DBPedia where we computed results corresponding to DeepAligned  ${}_{BERT}$  (DA  ${}_{BERT}$  ),DeepAligned  ${}_{SBERT}$  (DASBERT) using code provided by Zhang et al. (2021c) and user SBERT as PLM in DSSCC.
CLINC150 BANKING77
K	A	ACC	NMI	ARI	ACC	NMI	ARI
25%	\( \mathbf{D}\mathbf{A}_{\mathbf{B}\mathbf{E}\mathbf{T}\mathbf{R}} \)	73.71	88.71	64.27	48.88	70.45	36.81
\( \mathbf{D}\mathbf{A}_{\mathbf{S}\mathbf{B}\mathbf{E}\mathbf{T}\mathbf{R}} \)	67.78	86.50	57.10	57.0	75.0	45.80
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{C}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	75.72	89.12	66.72	55.52	72.73	42.11
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{S}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	80.36	91.43	72.83	64.93	80.17	53.60
50%	\( \mathbf{D}\mathbf{A}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	80.22	91.63	72.34	59.23	76.52	47.82
\( \mathbf{D}\mathbf{A}_{\mathbf{S}\mathbf{B}\mathbf{T}\mathbf{R}} \)	77.69	91.40	70.90	64.14	79.30	52.70
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{C}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	81.46	91.39	73.48	63.08	77.60	50.64
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{S}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	83.49	92.78	76.80	69.38	82.68	58.95
75%	\( \mathbf{D}\mathbf{A}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	86.01	94.03	79.82	64.90	79.56	53.64
\( \mathbf{D}\mathbf{A}_{\mathbf{S}\mathbf{B}\mathbf{T}\mathbf{R}} \)	85.89	94.20	79.83	74.08	83.80	63.30
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{C}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	87.91	93.87	81.09	69.82	81.24	58.09
\( \mathbf{D}\mathbf{S}\mathbf{S}\mathbf{C}\mathbf{S}_{\mathbf{B}\mathbf{T}\mathbf{R}} \)	88.47	94.50	82.40	75.15	85.04	64.83
Table 14: DA vs DSSCC with BERT and SBERT as PLM
Approach	K'	KPred	ACC	NMI	ARI	K'	KPred	ACC	NMI	ARI
DABERT	300	130	77.18	92.5	72.26	154	65.1	62.49	78.88	51.71
DASBERT	300	129.6	76.87	92.61	72.05	154	66.9	63.53	80.84	53.26
DSSCC-KMSBERT	79.0	92.72	73.58	71.77	84.29	62.13
DSSCC-CHSBERT	81.37	92.97	75.49	71.91	82.60	60.69
DSSCCSBERT	81.37	92.97	75.49	71.77	84.29	62.13
DABERT	450	189.2	83.81	93.57	79.54	231	99.3	63.98	79.93	53.76
DASBERT	450	190.1	82.57	93.85	79.28	231	96.8	66.20	82.11	56.98
DSSCC-KMSBERT	82.69	93.59	78.85	72.93	84.97	64.65
DSSCC-CHSBERT	84.59	93.64	80.44	72.60	82.78	62.86
DSSCCSBERT	84.59	93.64	80.44	72.93	84.97	64.65
DABERT	600	258.6	72.22	91.8	70.91	308	121.9	61.05	79.95	53.10
DASBERT	600	255.9	72.29	92.18	71.38	308	118.1	62.67	82.05	55.75
DSSCC-KMSBERT	73.42	92.07	72.19	67.56	84.43	61.87
DSSCC-CHSBERT	80.83	92.04	76.28	68.08	81.19	59.37
DSSCCSBERT	80.83	92.04	76.28	67.56	84.43	61.87
DSSCCSBERT	150	150	88.47	94.50	82.40	77	77	75.15	85.04	64.83
Table 15: Intent detection and discovery with unknown value of  $m$  for KIR=75%. We obtain results corresponding to DeepAligned  ${}_{BERT}$  (DA  ${}_{BERT}$  ) (Zhang et al.,2021c) and report results for DeepAligned  ${}_{SBERT}$  (DA  ${}_{SBERT}$  ) using code provided by the authors of Zhang et al. (2021c) with SBERT as the PLM.